diff --git a/404.html b/404.html
index a4a2d37..d885deb 100644
--- a/404.html
+++ b/404.html
@@ -78,7 +78,7 @@
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Lucy D’Agostino McGowan.</p>
+<p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 455695a..c88c57b 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -117,7 +117,7 @@ <h2 id="attribution">Attribution<a class="anchor" aria-label="anchor" href="#att
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 2ee8bae..8f794be 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -52,7 +52,7 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/LICENSE.html b/LICENSE.html
index f5935fe..f53ce9a 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -56,7 +56,7 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/apple-touch-icon-120x120.png b/apple-touch-icon-120x120.png
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diff --git a/apple-touch-icon-180x180.png b/apple-touch-icon-180x180.png
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diff --git a/apple-touch-icon-60x60.png b/apple-touch-icon-60x60.png
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diff --git a/apple-touch-icon-76x76.png b/apple-touch-icon-76x76.png
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diff --git a/apple-touch-icon.png b/apple-touch-icon.png
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diff --git a/authors.html b/authors.html
index eac846c..7ca3b09 100644
--- a/authors.html
+++ b/authors.html
@@ -51,7 +51,7 @@ <h2>Authors</h2>
           </p>
         </li>
         <li>
-          <p><strong>Malcolm Barrett</strong>. Contributor. <a href="https://orcid.org/0000-0003-0299-5825" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          <p><strong>Malcolm Barrett</strong>. Author. <a href="https://orcid.org/0000-0003-0299-5825" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
           </p>
         </li>
       </ul></div>
@@ -81,7 +81,7 @@ <h2 id="citation">Citation</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/favicon-16x16.png b/favicon-16x16.png
index 2e097ec..b5a1dac 100644
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diff --git a/favicon-32x32.png b/favicon-32x32.png
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diff --git a/index.html b/index.html
index 171ee1e..d931830 100644
--- a/index.html
+++ b/index.html
@@ -102,7 +102,7 @@
 </div>
 <!-- badges: start -->
 
-<p><strong>Authors:</strong> <a href="https://www.lucymcgowan.com/" class="external-link">Lucy D’Agostino McGowan</a><br><strong>License:</strong> <a href="https://opensource.org/license/mit/" class="external-link">MIT</a></p>
+<p><strong>Authors:</strong> <a href="https://www.lucymcgowan.com/" class="external-link">Lucy D’Agostino McGowan</a>, <a href="https://malco.io/" class="external-link">Malcolm Barrett</a><br><strong>License:</strong> <a href="https://opensource.org/license/mit/" class="external-link">MIT</a></p>
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
@@ -321,7 +321,7 @@ <h2 data-toc-skip>Citation</h2>
 <h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
 <li>Lucy D'Agostino McGowan <br><small class="roles"> Author, maintainer </small> <a href="https://orcid.org/0000-0002-6983-2759" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
-<li><a href="authors.html">More about authors...</a></li>
+<li>Malcolm Barrett <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0003-0299-5825" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
 </ul>
 </div>
 
@@ -339,7 +339,7 @@ <h2 data-toc-skip>Dev status</h2>
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Lucy D’Agostino McGowan.</p>
+<p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/news/index.html b/news/index.html
index 90f2bc4..9916a41 100644
--- a/news/index.html
+++ b/news/index.html
@@ -107,7 +107,7 @@ <h2 class="pkg-version" data-toc-text="0.1.1" id="tipr-011">tipr 0.1.1<a class="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/pkgdown.yml b/pkgdown.yml
index 91b406d..b3542a7 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,5 +2,5 @@ pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-02-06T17:31Z
+last_built: 2024-02-06T18:52Z
 
diff --git a/reference/adjust_coef.html b/reference/adjust_coef.html
index 3536e4b..be6f50d 100644
--- a/reference/adjust_coef.html
+++ b/reference/adjust_coef.html
@@ -125,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_coef_with_binary.html b/reference/adjust_coef_with_binary.html
index 1c52e04..d44d13c 100644
--- a/reference/adjust_coef_with_binary.html
+++ b/reference/adjust_coef_with_binary.html
@@ -132,7 +132,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_coef_with_r2.html b/reference/adjust_coef_with_r2.html
index 533c210..354b9ea 100644
--- a/reference/adjust_coef_with_r2.html
+++ b/reference/adjust_coef_with_r2.html
@@ -146,7 +146,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_hr.html b/reference/adjust_hr.html
index a474bff..30f3ac6 100644
--- a/reference/adjust_hr.html
+++ b/reference/adjust_hr.html
@@ -131,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_hr_with_binary.html b/reference/adjust_hr_with_binary.html
index 13c69b8..a7138d3 100644
--- a/reference/adjust_hr_with_binary.html
+++ b/reference/adjust_hr_with_binary.html
@@ -126,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_or.html b/reference/adjust_or.html
index c4fb08e..cf7dd93 100644
--- a/reference/adjust_or.html
+++ b/reference/adjust_or.html
@@ -131,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_or_with_binary.html b/reference/adjust_or_with_binary.html
index abbbf41..8a54b04 100644
--- a/reference/adjust_or_with_binary.html
+++ b/reference/adjust_or_with_binary.html
@@ -142,7 +142,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_rr.html b/reference/adjust_rr.html
index bf1b2cc..6de10a3 100644
--- a/reference/adjust_rr.html
+++ b/reference/adjust_rr.html
@@ -121,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/adjust_rr_with_binary.html b/reference/adjust_rr_with_binary.html
index f266197..ca35091 100644
--- a/reference/adjust_rr_with_binary.html
+++ b/reference/adjust_rr_with_binary.html
@@ -117,7 +117,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/e_value.html b/reference/e_value.html
index c0b8897..36b500d 100644
--- a/reference/e_value.html
+++ b/reference/e_value.html
@@ -83,7 +83,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/exdata_continuous.html b/reference/exdata_continuous.html
index 63ba7ee..87f6e49 100644
--- a/reference/exdata_continuous.html
+++ b/reference/exdata_continuous.html
@@ -76,7 +76,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/exdata_rr.html b/reference/exdata_rr.html
index 17bfdf6..e5aa7ec 100644
--- a/reference/exdata_rr.html
+++ b/reference/exdata_rr.html
@@ -76,7 +76,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/index.html b/reference/index.html
index d8a7d91..05f085b 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -203,7 +203,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/observed_bias_order.html b/reference/observed_bias_order.html
index 34382cd..e7991aa 100644
--- a/reference/observed_bias_order.html
+++ b/reference/observed_bias_order.html
@@ -76,7 +76,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/observed_bias_tbl.html b/reference/observed_bias_tbl.html
index 104e4be..6bcdeff 100644
--- a/reference/observed_bias_tbl.html
+++ b/reference/observed_bias_tbl.html
@@ -105,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/observed_bias_tip.html b/reference/observed_bias_tip.html
index 8c89ffe..bd012ae 100644
--- a/reference/observed_bias_tip.html
+++ b/reference/observed_bias_tip.html
@@ -98,7 +98,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/observed_covariate_e_value.html b/reference/observed_covariate_e_value.html
index 0f52131..af776c0 100644
--- a/reference/observed_covariate_e_value.html
+++ b/reference/observed_covariate_e_value.html
@@ -91,7 +91,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/pipe.html b/reference/pipe.html
index 3c80a4a..e76117b 100644
--- a/reference/pipe.html
+++ b/reference/pipe.html
@@ -59,7 +59,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/r_value.html b/reference/r_value.html
index 75618c0..34351d2 100644
--- a/reference/r_value.html
+++ b/reference/r_value.html
@@ -103,7 +103,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip.html b/reference/tip.html
index 329816a..f35c9a3 100644
--- a/reference/tip.html
+++ b/reference/tip.html
@@ -187,7 +187,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_coef.html b/reference/tip_coef.html
index 1f24edf..328b275 100644
--- a/reference/tip_coef.html
+++ b/reference/tip_coef.html
@@ -165,7 +165,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_coef_with_r2.html b/reference/tip_coef_with_r2.html
index 741fab3..fa4ae87 100644
--- a/reference/tip_coef_with_r2.html
+++ b/reference/tip_coef_with_r2.html
@@ -150,7 +150,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_hr.html b/reference/tip_hr.html
index 9ac1b9a..4afbaac 100644
--- a/reference/tip_hr.html
+++ b/reference/tip_hr.html
@@ -156,7 +156,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_hr_with_binary.html b/reference/tip_hr_with_binary.html
index 850bd9d..47d6d59 100644
--- a/reference/tip_hr_with_binary.html
+++ b/reference/tip_hr_with_binary.html
@@ -147,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_or.html b/reference/tip_or.html
index 73958db..19944dd 100644
--- a/reference/tip_or.html
+++ b/reference/tip_or.html
@@ -179,7 +179,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_or_with_binary.html b/reference/tip_or_with_binary.html
index 1f9ca83..e629072 100644
--- a/reference/tip_or_with_binary.html
+++ b/reference/tip_or_with_binary.html
@@ -147,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_rr.html b/reference/tip_rr.html
index b4696f4..2e64cc7 100644
--- a/reference/tip_rr.html
+++ b/reference/tip_rr.html
@@ -146,7 +146,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_rr_with_binary.html b/reference/tip_rr_with_binary.html
index 896bf87..852e2e7 100644
--- a/reference/tip_rr_with_binary.html
+++ b/reference/tip_rr_with_binary.html
@@ -112,7 +112,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tip_with_binary.html b/reference/tip_with_binary.html
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--- a/reference/tip_with_binary.html
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@@ -204,7 +204,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/tipr-package.html b/reference/tipr-package.html
index 5bce413..f01a160 100644
--- a/reference/tipr-package.html
+++ b/reference/tipr-package.html
@@ -83,7 +83,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Lucy D’Agostino McGowan.</p>
+  <p></p><p>Developed by Lucy D’Agostino McGowan, Malcolm Barrett.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/search.json b/search.json
index d188bfe..99d0d30 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement lucydagostino@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. 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Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 Lucy D’Agostino McGowan Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Lucy D'Agostino McGowan. Author, maintainer. Malcolm Barrett. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"McGowan LD (2022). “tipr: R package sensitivity analyses unmeasured confounders.” Journal Open Source Software, 7(77), 4495. doi:10.21105/joss.04495.","code":"@Article{,   title = {tipr: An R package for sensitivity analyses for unmeasured confounders},   author = {Lucy D'Agostino McGowan},   year = {2022},   journal = {Journal of Open Source Software},   volume = {7},   number = {77},   pages = {4495},   doi = {10.21105/joss.04495}, }"},{"path":"/index.html","id":"tipr-r-tools-for-tipping-point-sensitivity-analyses-","dir":"","previous_headings":"","what":"Tipping Point Analyses","title":"Tipping Point Analyses","text":"Authors: Lucy D’Agostino McGowanLicense: MIT","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tipping Point Analyses","text":"Install CRAN version install development version GitHub:","code":"install.packages(\"tipr\") # install.packages(devtools) devtools::install_github(\"r-causal/tipr\") library(tipr)"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Tipping Point Analyses","text":"fitting model, can determine unmeasured confounder needed tip analysis. unmeasured confounder determined two quantities, relationship exposure unmeasured confounder (unmeasured confounder continuous, indicated exposure_confounder_effect, binary, exposed_confounder_prev unexposed_confounder_prev), relationship unmeasured confounder outcome confounder_outcome_effect. Using 📦, can fix one solve . Alternatively, can fix solve n, , many unmeasured confounders magnitude tip analysis. package comes example data sets. example, use exdata_rr. data set simulated two confounders, one “measured” (thus usable main analysis, called measured_confounder) one “unmeasured” (access simulated data, ordinarily , variable called .unmeasured_confounder). Using example data exdata_rr, can estimate exposure-outcome relationship using measured confounder follows: see example, exposure-outcome relationship 1.5 (95% CI: 1.1, 2.1). Note, practice estimating effect exposure binary outcome using GLM Poisson distribution log link function, important use sandwich estimator appropriately estimate variability (can done R using sandwich package), case gives similar result (95% CI: 1.1, 2.0).","code":"mod <- glm(outcome ~ exposure + measured_confounder, data = exdata_rr,             family = poisson)  mod |>   broom::tidy(exponentiate = TRUE, conf.int = TRUE) ## # A tibble: 3 × 7 ##   term                estimate std.error statistic   p.value conf.low conf.high ##   <chr>                  <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> ## 1 (Intercept)           0.0366    0.151     -21.9  2.56e-106   0.0269    0.0486 ## 2 exposure              1.49      0.166       2.43 1.52e-  2   1.09      2.08   ## 3 measured_confounder   2.43      0.0754     11.7  7.51e- 32   2.09      2.81"},{"path":"/index.html","id":"continuous-unmeasured-confounder-example","dir":"","previous_headings":"","what":"Continuous unmeasured confounder example","title":"Tipping Point Analyses","text":"interested continuous unmeasured confounder, use tip_with_continuous() function. Let’s assume unmeasured confounder normally distributed mean 0.5 exposed group 0 unexposed (unit variance ), resulting mean difference 0.5 (exposure_confounder_effect = 0.5), let’s solve relationship unmeasured confounder outcome needed tip analysis (case, solving confounder_outcome_effect). hypothetical unobserved continuous confounder scaled mean difference exposure groups 0.5 need relationship least 2.25 outcome tip analysis point estimate. hypothetical unobserved continuous confounder scaled mean difference exposure groups 0.5 need relationship least 1.19 outcome tip analysis 5% level, rendering inconclusive. simulated data, can see true unmeasured confounder looked like. First calculate difference scaled means. Now can refit model unmeasured confounder included. According tipping point result, long risk ratio unmeasured confounder outcome model greater 2.25, result observed “tipped” (point estimate cross null). Notice .unmeasured_confounder effect 2.42 (greater 2.25 calculated needed render result null) , expected, point estimate exposure crossed null (now less 1).","code":"tip(effect_observed = 1.5, exposure_confounder_effect = 0.5) ## ℹ The observed effect (1.5) WOULD be tipped by 1 unmeasured confounder with the ##   following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.5 ## • estimated relationship between the unmeasured confounder and the outcome: ##   2.25  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1             1.5                     0.5                   2.25 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl> tip(effect_observed = 1.09, exposure_confounder_effect = 0.5) ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.5 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.19  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                     0.5                   1.19 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl> exdata_rr |>   dplyr::group_by(exposure) |>   dplyr::summarise(m = mean(.unmeasured_confounder / sd(.unmeasured_confounder))) |>   tidyr::pivot_wider(names_from = exposure,               values_from = m,               names_prefix = \"u_\") |>   dplyr::summarise(estimate = u_1 - u_0) ## # A tibble: 1 × 1 ##   estimate ##      <dbl> ## 1    0.494 mod_true <- glm(   outcome ~ exposure + measured_confounder + .unmeasured_confounder,    data = exdata_rr,    family = poisson)  mod_true |>   broom::tidy(exponentiate = TRUE, conf.int = TRUE) ## # A tibble: 4 × 7 ##   term                 estimate std.error statistic   p.value conf.low conf.high ##   <chr>                   <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> ## 1 (Intercept)            0.0245    0.163    -22.7   1.49e-114   0.0176    0.0334 ## 2 exposure               0.921     0.172     -0.477 6.34e-  1   0.660     1.30   ## 3 measured_confounder    2.44      0.0746    11.9   6.95e- 33   2.11      2.82   ## 4 .unmeasured_confoun…   2.42      0.0742    11.9   1.35e- 32   2.09      2.80"},{"path":"/index.html","id":"binary-unmeasured-confounder-example","dir":"","previous_headings":"","what":"Binary unmeasured confounder example","title":"Tipping Point Analyses","text":"Now interested binary unmeasured confounder, use tip_with_binary() function. Let’s assume unmeasured confounder prevalent 25% exposed population (exposed_confounder_prev = 0.25) 10% unexposed population (unexposed_confounder_prev = 0.10) – let’s solve relationship unmeasured confounder outcome needed tip analysis (confounder_outcome_effect). hypothetical unobserved binary confounder prevalent 10% unexposed population 25% exposed population need relationship outcome 1.64 tip analysis 5% level, rendering inconclusive.","code":"tip_with_binary(effect_observed = 1.09,                  exposed_confounder_prev = 0.25,                  unexposed_confounder_prev = 0.10) ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated prevalence of the unmeasured confounder in the exposed population: ##   0.25 ## • estimated prevalence of the unmeasured confounder in the unexposed ##   population: 0.1 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.64  ## # A tibble: 1 × 6 ##   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                    0.25                    0.1 ## # ℹ abbreviated name: ¹​unexposed_confounder_prev ## # ℹ 2 more variables: confounder_outcome_effect <dbl>, ## #   n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"many-unmeasured-confounders","dir":"","previous_headings":"","what":"Many unmeasured confounders","title":"Tipping Point Analyses","text":"Suppose concerned many small, independent, continuous, unmeasured confounders present. take 7 independent standardized Normal unmeasured confounders mean difference exposure groups 0.25 relationship outcome 1.05 tip observed analysis 5% level, rendering inconclusive.","code":"tip(effect_observed = 1.09,      exposure_confounder_effect = 0.25,      confounder_outcome_effect = 1.05) ## ℹ The observed effect (1.09) WOULD be tipped by 7 unmeasured confounders with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.25 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.05  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                    0.25                   1.05 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"integration-with-broom","dir":"","previous_headings":"","what":"Integration with broom","title":"Tipping Point Analyses","text":"functions created easily integrate models tidied using broom package. necessary use functions, nice feature choose . example logistic regression fit glm tidied tidy function broom can directly fed tip() function.","code":"if (requireNamespace(\"broom\", quietly = TRUE) &&  requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(outcome ~ exposure + measured_confounder, data = exdata_rr,             family = poisson) |>     broom::tidy(conf.int = TRUE, exponentiate = TRUE) |>     dplyr::filter(term == \"exposure\") |>     dplyr::pull(conf.low) |>     tip(confounder_outcome_effect = 2.5) } ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.09 ## • estimated relationship between the unmeasured confounder and the outcome: 2.5  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                  0.0907                    2.5 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Tipping Point Analyses","text":"Please note tipr project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/reference/adjust_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"Adjust observed regression coefficient normally distributed confounder","code":""},{"path":"/reference/adjust_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"","code":"adjust_coef(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )  adjust_coef_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"Data frame.","code":""},{"path":"/reference/adjust_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"","code":"## Update an observed coefficient of 0.5 with an unmeasured confounder ## with a difference in scaled means between exposure groups of 0.2 ## and coefficient of 0.3 adjust_coef(0.5, 0.2, 0.3) #> ℹ The observed effect (0.5) is updated to 0.44 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.2 #> • estimated relationship between the unmeasured confounder and the outcome: 0.3 #> # A tibble: 1 × 4 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1            0.44             0.5                     0.2                    0.3 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect"},{"path":"/reference/adjust_coef_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"Adjust observed coefficient regression model binary confounder","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"","code":"adjust_coef_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   loglinear = FALSE,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_coef_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"effect_observed Numeric. Observed exposure - outcome effect loglinear model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. loglinear Logical. Calculate adjusted coefficient loglinear model instead linear model (default). loglinear = FALSE, adjust_coef_with_binary() equivalent adjust_coef() exposure_confounder_effect difference prevalences. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"","code":"adjust_coef_with_binary(1.1, 0.5, 0.3, 1.3) #> ℹ The observed effect (1.1) is updated to 0.84 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.3 #> • estimated relationship between the unmeasured confounder and the outcome: 1.3 #> # A tibble: 1 × 4 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1            0.84             1.1                     0.2                    1.3 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect"},{"path":"/reference/adjust_coef_with_r2.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"function wraps sensemakr::adjusted_estimate() sensemakr::adjusted_se() functions.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"","code":"adjust_coef_with_r2(   effect_observed,   se,   df,   confounder_exposure_r2,   confounder_outcome_r2,   verbose = getOption(\"tipr.verbose\", TRUE),   alpha = 0.05,   ... )"},{"path":"/reference/adjust_coef_with_r2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. confounder_exposure_r2 Numeric value 0 1. assumed partial R2 unobserved confounder exposure given measured covariates. confounder_outcome_r2 Numeric value 0 1. assumed partial R2 unobserved confounder outcome given exposure measured covariates. verbose Logical. Indicates whether print informative message. Default: TRUE alpha Significance level. Default = 0.05. ... Optional arguments passed sensemakr::adjusted_estimate() function.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"data frame.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"Carlos Cinelli, Jeremy Ferwerda Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"","code":"adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1) #> # A tibble: 1 × 10 #>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed #>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl> #> 1           0.427       0.233       0.621             0.5       0.302 #> # ℹ 5 more variables: ub_observed <dbl>, se_observed <dbl>, df_observed <dbl>, #> #   confounder_exposure_r2 <dbl>, confounder_outcome_r2 <dbl>"},{"path":"/reference/adjust_hr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"Adjust observed hazard ratio normally distributed confounder","code":""},{"path":"/reference/adjust_hr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"","code":"adjust_hr(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )  adjust_hr_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/adjust_hr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_hr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"Data frame.","code":""},{"path":"/reference/adjust_hr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"","code":"adjust_hr(0.9, -0.9, 1.3) #> ℹ The observed effect (HR: 0.9) is updated to HR: 1.14 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: -0.9 #> • estimated relationship (HR) between the unmeasured confounder and the #>   outcome: 1.3 #> # A tibble: 1 × 4 #>   hr_adjusted hr_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1        1.14         0.9                       -0.9                       1.3"},{"path":"/reference/adjust_hr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"Adjust observed hazard ratio binary confounder","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"","code":"adjust_hr_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/adjust_hr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"","code":"adjust_hr_with_binary(0.8, 0.1, 0.5, 1.8) #> ℹ The observed effect (HR: 0.8) is updated to HR: 1.04 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.5 #> • estimated relationship between the unmeasured confounder and the outcome: 1.8 #> # A tibble: 1 × 5 #>   hr_adjusted hr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1        1.04         0.8                     0.1                       0.5 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/adjust_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"Adjust observed odds ratio normally distributed confounder","code":""},{"path":"/reference/adjust_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"","code":"adjust_or(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )  adjust_or_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/adjust_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"Data frame.","code":""},{"path":"/reference/adjust_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"","code":"adjust_or(1.2, 0.9, 1.3) #> ℹ The observed effect (OR: 1.2) is updated to OR: 0.95 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.9 #> • estimated relationship (OR) between the unmeasured confounder and the #>   outcome: 1.3 #> # A tibble: 1 × 4 #>   or_adjusted or_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1       0.948         1.2                        0.9                       1.3"},{"path":"/reference/adjust_or_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"Adjust observed odds ratio binary confounder","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"","code":"adjust_or_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/adjust_or_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"","code":"adjust_or_with_binary(3, 1, 0, 3) #> • The observed effect (OR: 3) is updated to OR: 1 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 3 #> # A tibble: 1 × 5 #>   or_adjusted or_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1           1           3                       1                         0 #> # ℹ 1 more variable: confounder_outcome_effect <dbl> adjust_or_with_binary(3, 1, 0, 3, or_correction = TRUE) #> • The observed effect (RR: 1.73) is updated to RR: 1 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.73 #> ℹ You opted to use the odds ratio correction to convert your odds ratios to #>   approximate risk ratios. This is a good idea if the outcome is common (>15%) #> # A tibble: 1 × 5 #>   rr_adjusted rr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1           1        1.73                       1                         0 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/adjust_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"Adjust observed risk ratio normally distributed confounder","code":""},{"path":"/reference/adjust_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"","code":"adjust_rr(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = TRUE )  adjust_rr_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = TRUE )"},{"path":"/reference/adjust_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"Data frame.","code":""},{"path":"/reference/adjust_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"","code":"adjust_rr(1.2, 0.5, 1.1) #> ℹ The observed effect (RR: 1.2) is updated to RR: 1.14 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.5 #> • estimated relationship (RR) between the unmeasured confounder and the #>   outcome: 1.1 #> # A tibble: 1 × 4 #>   rr_adjusted rr_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1        1.14         1.2                        0.5                       1.1"},{"path":"/reference/adjust_rr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"Adjust observed risk ratio binary confounder","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"","code":"adjust_rr_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_rr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"","code":"adjust_rr_with_binary(1.1, 0.5, 0.3, 1.3) #> The observed effect (1.1) is updated to 1.04 by a confounder with the following #> specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.3 #> • estimated relationship between the unmeasured confounder and the outcome: 1.3 #> # A tibble: 1 × 5 #>   rr_adjusted rr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1        1.04         1.1                     0.5                       0.3 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/e_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate an E-value — e_value","title":"Calculate an E-value — e_value","text":"Calculate E-value","code":""},{"path":"/reference/e_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate an E-value — e_value","text":"","code":"e_value(effect_observed)"},{"path":"/reference/e_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate an E-value — e_value","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound.","code":""},{"path":"/reference/e_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate an E-value — e_value","text":"Numeric value","code":""},{"path":"/reference/e_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate an E-value — e_value","text":"","code":"e_value(0.9) #> [1] 1.462475 e_value(1.3) #> [1] 1.9245"},{"path":"/reference/exdata_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Example Data (Continuous Outcome) — exdata_continuous","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"data set simulated two Normally distributed confounders, one \"measured\" one \"unmeasured\", exposure, outcome. \"true\" causal effect exposure outcome, accounting measured unmeasured confounders, 0.","code":""},{"path":"/reference/exdata_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"","code":"exdata_continuous"},{"path":"/reference/exdata_continuous.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"data frame 2,000 rows 4 columns: .unmeasured_confounder: simulated unmeasured confounder measured_confounder: simulated measured confounder exposure outcome","code":""},{"path":"/reference/exdata_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Example Data (Risk Ratio) — exdata_rr","title":"Example Data (Risk Ratio) — exdata_rr","text":"data set simulated two Normally distributed confounders, one \"measured\" one \"unmeasured\", exposure, outcome. \"true\" causal effect exposure outcome, accounting measured unmeasured confounders, 0.","code":""},{"path":"/reference/exdata_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example Data (Risk Ratio) — exdata_rr","text":"","code":"exdata_rr"},{"path":"/reference/exdata_rr.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example Data (Risk Ratio) — exdata_rr","text":"data frame 2,000 rows 4 columns: .unmeasured_confounder: simulated unmeasured confounder measured_confounder: simulated measured confounder exposure outcome","code":""},{"path":"/reference/observed_bias_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Order observed bias data frame for plotting — observed_bias_order","title":"Order observed bias data frame for plotting — observed_bias_order","text":"Order observed bias data frame plotting","code":""},{"path":"/reference/observed_bias_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Order observed bias data frame for plotting — observed_bias_order","text":"","code":"observed_bias_order(d, by)"},{"path":"/reference/observed_bias_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Order observed bias data frame for plotting — observed_bias_order","text":"d Observed bias data frame. Must columns dropped type Character. Variable d order .","code":""},{"path":"/reference/observed_bias_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Order observed bias data frame for plotting — observed_bias_order","text":"Data frame correct order","code":""},{"path":"/reference/observed_bias_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"Create data frame assist creating observed bias plot","code":""},{"path":"/reference/observed_bias_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"","code":"observed_bias_tbl(ps_mod, outcome_mod, drop_list = NULL)"},{"path":"/reference/observed_bias_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"ps_mod Model object propensity score model outcome_mod Model object outcome model drop_list Named list covariates groups covariates drop NULL, default dropping covariate one time.","code":""},{"path":"/reference/observed_bias_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"Data frame following columns: dropped: covariate group covariates dropped type: Explanation dropped, whether refers single covariate (covariate) group covariates (group) ps_formula: new formula updated propensity score model outcome_formula: new formula updated outcome model ps_model: new model object updated propensity score model p: updated propensity score","code":""},{"path":"/reference/observed_bias_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"","code":"ps_mod <- glm(am ~ mpg + cyl + I(hp^2), data = mtcars) outcome_mod <- lm(qsec ~ am + hp + disp + wt, data = mtcars) observed_bias_tbl(  ps_mod,  outcome_mod,  drop_list = list(    group_one = c(\"mpg\", \"hp\"),    group_two = c(\"cyl\", \"wt\")    )  ) #> # A tibble: 2 × 6 #>   dropped   type  ps_formula outcome_formula ps_model p          #>   <chr>     <chr> <list>     <list>          <list>   <list>     #> 1 group_one group <formula>  <formula>       <glm>    <dbl [32]> #> 2 group_two group <formula>  <formula>       <glm>    <dbl [32]>"},{"path":"/reference/observed_bias_tip.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"Create data frame combine observed bias data frame demonstrating hypothetical unmeasured confounder","code":""},{"path":"/reference/observed_bias_tip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"","code":"observed_bias_tip(   tip,   point_estimate,   lb,   ub,   tip_desc = \"Hypothetical unmeasured confounder\" )"},{"path":"/reference/observed_bias_tip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"tip Numeric. Value like tip . point_estimate Numeric. Result estimate full model. lb Numeric. Result lower bound full model. ub Numeric. Result upper bound full model. tip_desc Character. description tipping point.","code":""},{"path":"/reference/observed_bias_tip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"data frame five columns: dropped: input tip_desc type: Explanation dropped, tip clarify calculated tipping point. point_estimate: shifted point estimate lb: shifted lower bound ub: shifted upper bound","code":""},{"path":"/reference/observed_covariate_e_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Observed Covariate E-value — observed_covariate_e_value","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"Calculate Observed Covariate E-value","code":""},{"path":"/reference/observed_covariate_e_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"","code":"observed_covariate_e_value(lb, ub, lb_adj, ub_adj, transform = NULL)"},{"path":"/reference/observed_covariate_e_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"lb Numeric. lower bound full model ub Numeric. upper bound full model lb_adj Numeric. lower bound adjusted model ub_adj Numeric. upper bound adjusted model transform Character. effect odds ratio hazard ratio, perform transformation suggested VanderWeele Ding. Allowed values : \"\" \"HR\"","code":""},{"path":"/reference/observed_covariate_e_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"Observed Covariate E-value","code":""},{"path":"/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"Pipe operator","code":""},{"path":"/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"/reference/r_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Robustness value — r_value","title":"Robustness value — r_value","text":"function wraps sensemakr::robustness_value() function","code":""},{"path":"/reference/r_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Robustness value — r_value","text":"","code":"r_value(effect_observed, se, df, ...)"},{"path":"/reference/r_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Robustness value — r_value","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. ... Optional arguments passed sensemakr::robustness_value() function.","code":""},{"path":"/reference/r_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Robustness value — r_value","text":"Numeric. Robustness value","code":""},{"path":"/reference/r_value.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Robustness value — r_value","text":"Carlos Cinelli, Jeremy Ferwerda Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr","code":""},{"path":"/reference/r_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Robustness value — r_value","text":"","code":"r_value(0.5, 0.1, 102) #> [1] 0.387467"},{"path":"/reference/tip.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a result with a normally distributed confounder. — tip","title":"Tip a result with a normally distributed confounder. — tip","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a result with a normally distributed confounder. — tip","text":"","code":"tip(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_c(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )"},{"path":"/reference/tip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a result with a normally distributed confounder. — tip","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE correction_factor Character string. Options \"none\", \"hr\", \"\". common outcomes (>15%), odds ratio hazard ratio good estimate risk ratio. cases, can apply correction factor. supplying hazard ratio common outcome, set \"hr\"; supplying odds ratio common outcome, set \"\"; supplying risk ratio outcome rare, set \"none\" (default).","code":""},{"path":"/reference/tip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a result with a normally distributed confounder. — tip","text":"Data frame.","code":""},{"path":"/reference/tip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a result with a normally distributed confounder. — tip","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip(confounder_outcome_effect = 2.5) } #> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: 0.13 #> • estimated relationship between the unmeasured confounder and the outcome: 2.5 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.13                   0.133                    2.5 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a linear model coefficient with a continuous confounder. — tip_coef","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"","code":"tip_coef(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )  tip_coef_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposure_confounder_effect Numeric. Estimated scaled mean difference unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"Data frame.","code":""},{"path":"/reference/tip_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_coef(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   -0.6 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1             1.2                         -2                      -0.6 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_coef(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = -0.05) #> ℹ The observed effect (1.2) WOULD be tipped by 12 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   -0.05 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1             1.2                         -2                     -0.05 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   lm(wt ~ mpg, data = mtcars) %>%    broom::tidy(conf.int = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_coef(confounder_outcome_effect = 2.5) } #> ℹ The observed effect (-0.17) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -0.07 #> • estimated relationship between the unmeasured confounder and the outcome: 2.5 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1          -0.171                    -0.0684                       2.5 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_coef_with_r2.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"Choose one following, estimated: confounder_exposure_r2 confounder_outcome_r2","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"","code":"tip_coef_with_r2(   effect_observed,   se,   df,   confounder_exposure_r2 = NULL,   confounder_outcome_r2 = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   alpha = 0.05,   tip_bound = FALSE,   ... )"},{"path":"/reference/tip_coef_with_r2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. confounder_exposure_r2 Numeric value 0 1. assumed partial R2 unobserved confounder exposure given measured covariates. confounder_outcome_r2 Numeric value 0 1. assumed partial R2 unobserved confounder outcome given exposure measured covariates. verbose Logical. Indicates whether print informative message. Default: TRUE alpha Significance level. Default = 0.05. tip_bound want tip bound? Default = FALSE, tip point estimate ... Optional arguments passed sensemakr::adjusted_estimate() function.","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"data frame.","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"","code":"tip_coef_with_r2(0.5, 0.1, 102, 0.5) #> # A tibble: 1 × 10 #>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed #>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl> #> 1        5.55e-17      -0.245       0.245             0.5       0.302 #> # ℹ 5 more variables: ub_observed <dbl>, se_observed <dbl>, df_observed <dbl>, #> #   confounder_exposure_r2 <dbl>, confounder_outcome_r2 <dbl>"},{"path":"/reference/tip_hr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_hr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"","code":"tip_hr(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )  tip_hr_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/tip_hr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_hr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"Data frame.","code":""},{"path":"/reference/tip_hr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_hr(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_hr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_hr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"","code":"tip_hr_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/tip_hr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"Data frame.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"","code":"tip_hr_with_binary(0.9, 0.9, 0.1) #> ℹ The observed effect (0.9) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.9 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.1 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.88 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             0.9                     0.9                    0.1 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"","code":"tip_or(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )  tip_or_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/tip_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"Data frame.","code":""},{"path":"/reference/tip_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_or(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_or(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_or(confounder_outcome_effect = 2.5, or_correction = TRUE) } #> ℹ The observed effect (1.06) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: 0.13 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.58 #> ℹ You opted to use the odds ratio correction to convert your odds ratios to #>   approximate risk ratios. This is a good idea if the outcome is common (>15%). #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.06                   0.133                   1.58 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_or_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"","code":"tip_or_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/tip_or_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"Data frame.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"","code":"tip_or_with_binary(0.9, 0.9, 0.1) #> ℹ The observed effect (0.9) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.9 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.1 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.88 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             0.9                     0.9                    0.1 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"","code":"tip_rr(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )  tip_rr_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"Data frame.","code":""},{"path":"/reference/tip_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_rr(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_rr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_rr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_rr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"","code":"tip_rr_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_rr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a result with a binary confounder. — tip_with_binary","title":"Tip a result with a binary confounder. — tip_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a result with a binary confounder. — tip_with_binary","text":"","code":"tip_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_b(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )"},{"path":"/reference/tip_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a result with a binary confounder. — tip_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE correction_factor Character string. Options \"none\", \"hr\", \"\". common outcomes (>15%), odds ratio hazard ratio good estimate risk ratio. cases, can apply correction factor. supplying hazard ratio common outcome, set \"hr\"; supplying odds ratio common outcome, set \"\"; supplying risk ratio outcome rare, set \"none\" (default).","code":""},{"path":"/reference/tip_with_binary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tip a result with a binary confounder. — tip_with_binary","text":"tip_b() alias tip_with_binary().","code":""},{"path":"/reference/tip_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a result with a binary confounder. — tip_with_binary","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 1.4 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                     0.5                      0 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_with_binary(1.2,   exposed_confounder_prev = 0.5,   unexposed_confounder_prev = 0,   confounder_outcome_effect = 1.1) #> ℹ The observed effect (1.2) WOULD be tipped by 4 unmeasured confounders with #>   the following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 1.1 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                     0.5                      0 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15) } #> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.12 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.15 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.13                       1                  0.119 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tipr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tipr — tipr-package","title":"tipr — tipr-package","text":"tipr package.","code":""},{"path":"/reference/tipr-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tipr — tipr-package","text":"D'Agostino McGowan, L, (2022). tipr: R package sensitivity analyses unmeasured confounders. Journal Open Source Software, 7(77), 4495. D’Agostino McGowan, L. (2022). Sensitivity Analyses Unmeasured Confounders. Current Epidemiology Reports, 9(4), 361-375. VanderWeele, TJ, Peng D (2017). Sensitivity Analysis Observational Research: Introducing E-Value. Ann Intern Med, 167(4), 268–74. Cinelli, C, & Hazlett, C (2020). Making sense sensitivity: Extending omitted variable bias. Journal Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39–67. Lin, DY, Psaty, BM, & Kronmal, RA. (1998). Assessing sensitivity regression results unmeasured confounders observational studies. Biometrics, 54(3), 948–963.","code":""},{"path":[]},{"path":"/reference/tipr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tipr — tipr-package","text":"Maintainer: Lucy D'Agostino McGowan lucydagostino@gmail.com (ORCID) contributors: Malcolm Barrett malcolmbarrett@gmail.com (ORCID) [contributor]","code":""},{"path":"/news/index.html","id":"tipr-102","dir":"Changelog","previous_headings":"","what":"tipr 1.0.2","title":"tipr 1.0.2","text":"adjust_coef_with_binary() now assumes coefficient linear model rather loglinear. Use loglinear = TRUE get old behavior. (#12, @malcolmbarrett) Fixed roxygen issue package documentation","code":""},{"path":"/news/index.html","id":"tipr-101","dir":"Changelog","previous_headings":"","what":"tipr 1.0.1","title":"tipr 1.0.1","text":"CRAN release: 2022-09-05 Fixed bug, functions based adjust_coef_with_binary function old parameter names (exposed_p unexposed_p). changed match new updates version 1.0.0 now exposed_confounder_prev unexposed_confounder_prev. Change “relative risk” “risk ratio” documentation. Add new JOSS citation","code":""},{"path":"/news/index.html","id":"tipr-100","dir":"Changelog","previous_headings":"","what":"tipr 1.0.0","title":"tipr 1.0.0","text":"CRAN release: 2022-08-06 Breaking changes. names several arguments changed increased clarity: effect -> effect_observed outcome_association -> confounder_outcome_effect smd -> exposure_confounder_effect exposed_p -> exposed_confounder_prev unexposed_p -> unexposed_confounder_prev exposure_r2 -> confounder_exposure_r2 outcome_r2 -> confounder_outcome_r2 Added two new example datasets: exdata_continuous exdata_rr","code":""},{"path":"/news/index.html","id":"tipr-042","dir":"Changelog","previous_headings":"","what":"tipr 0.4.2","title":"tipr 0.4.2","text":"Make output tibble names consistent (adjusted_effect -> effect_adjusted)","code":""},{"path":"/news/index.html","id":"tipr-041","dir":"Changelog","previous_headings":"","what":"tipr 0.4.1","title":"tipr 0.4.1","text":"CRAN release: 2022-05-05 Add additional functions specify *_with_continuous() (long form , function names, default unmeasured confounder Normally distributed) Change tip_lm() tip_coef().","code":""},{"path":"/news/index.html","id":"tipr-040","dir":"Changelog","previous_headings":"","what":"tipr 0.4.0","title":"tipr 0.4.0","text":"CRAN release: 2022-04-16 Changed name lm_tip() tip_lm() API fundamentally updated functions now take numeric value first argument rather data frame. Added adjust_* functions allow specification unmeasured confounder qualities without tipping Split tip_* functions hazard ratio, odds ratio, relative risk Add R2 parameterization tip_coef_with_r2(), adjust_coef_with_r2(), r_value()","code":""},{"path":"/news/index.html","id":"tipr-030","dir":"Changelog","previous_headings":"","what":"tipr 0.3.0","title":"tipr 0.3.0","text":"CRAN release: 2021-09-10 Added ability perform sensitivity analyses linear models via lm_tip()","code":""},{"path":"/news/index.html","id":"tipr-020","dir":"Changelog","previous_headings":"","what":"tipr 0.2.0","title":"tipr 0.2.0","text":"CRAN release: 2020-11-16 Updated several function parameter names. main functions now tip() tip_with_binary(). parameter names self-explanatory. API fundamentally updated functions now take data frame first argument. now explicit (required) integration broom package.","code":""},{"path":"/news/index.html","id":"tipr-011","dir":"Changelog","previous_headings":"","what":"tipr 0.1.1","title":"tipr 0.1.1","text":"CRAN release: 2017-11-28 initial CRAN release","code":""}]
+[{"path":[]},{"path":"/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement lucydagostino@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 Lucy D’Agostino McGowan Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Lucy D'Agostino McGowan. Author, maintainer. Malcolm Barrett. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"McGowan LD (2022). “tipr: R package sensitivity analyses unmeasured confounders.” Journal Open Source Software, 7(77), 4495. doi:10.21105/joss.04495.","code":"@Article{,   title = {tipr: An R package for sensitivity analyses for unmeasured confounders},   author = {Lucy D'Agostino McGowan},   year = {2022},   journal = {Journal of Open Source Software},   volume = {7},   number = {77},   pages = {4495},   doi = {10.21105/joss.04495}, }"},{"path":"/index.html","id":"tipr-r-tools-for-tipping-point-sensitivity-analyses-","dir":"","previous_headings":"","what":"Tipping Point Analyses","title":"Tipping Point Analyses","text":"Authors: Lucy D’Agostino McGowan, Malcolm BarrettLicense: MIT","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tipping Point Analyses","text":"Install CRAN version install development version GitHub:","code":"install.packages(\"tipr\") # install.packages(devtools) devtools::install_github(\"r-causal/tipr\") library(tipr)"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Tipping Point Analyses","text":"fitting model, can determine unmeasured confounder needed tip analysis. unmeasured confounder determined two quantities, relationship exposure unmeasured confounder (unmeasured confounder continuous, indicated exposure_confounder_effect, binary, exposed_confounder_prev unexposed_confounder_prev), relationship unmeasured confounder outcome confounder_outcome_effect. Using 📦, can fix one solve . Alternatively, can fix solve n, , many unmeasured confounders magnitude tip analysis. package comes example data sets. example, use exdata_rr. data set simulated two confounders, one “measured” (thus usable main analysis, called measured_confounder) one “unmeasured” (access simulated data, ordinarily , variable called .unmeasured_confounder). Using example data exdata_rr, can estimate exposure-outcome relationship using measured confounder follows: see example, exposure-outcome relationship 1.5 (95% CI: 1.1, 2.1). Note, practice estimating effect exposure binary outcome using GLM Poisson distribution log link function, important use sandwich estimator appropriately estimate variability (can done R using sandwich package), case gives similar result (95% CI: 1.1, 2.0).","code":"mod <- glm(outcome ~ exposure + measured_confounder, data = exdata_rr,             family = poisson)  mod |>   broom::tidy(exponentiate = TRUE, conf.int = TRUE) ## # A tibble: 3 × 7 ##   term                estimate std.error statistic   p.value conf.low conf.high ##   <chr>                  <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> ## 1 (Intercept)           0.0366    0.151     -21.9  2.56e-106   0.0269    0.0486 ## 2 exposure              1.49      0.166       2.43 1.52e-  2   1.09      2.08   ## 3 measured_confounder   2.43      0.0754     11.7  7.51e- 32   2.09      2.81"},{"path":"/index.html","id":"continuous-unmeasured-confounder-example","dir":"","previous_headings":"","what":"Continuous unmeasured confounder example","title":"Tipping Point Analyses","text":"interested continuous unmeasured confounder, use tip_with_continuous() function. Let’s assume unmeasured confounder normally distributed mean 0.5 exposed group 0 unexposed (unit variance ), resulting mean difference 0.5 (exposure_confounder_effect = 0.5), let’s solve relationship unmeasured confounder outcome needed tip analysis (case, solving confounder_outcome_effect). hypothetical unobserved continuous confounder scaled mean difference exposure groups 0.5 need relationship least 2.25 outcome tip analysis point estimate. hypothetical unobserved continuous confounder scaled mean difference exposure groups 0.5 need relationship least 1.19 outcome tip analysis 5% level, rendering inconclusive. simulated data, can see true unmeasured confounder looked like. First calculate difference scaled means. Now can refit model unmeasured confounder included. According tipping point result, long risk ratio unmeasured confounder outcome model greater 2.25, result observed “tipped” (point estimate cross null). Notice .unmeasured_confounder effect 2.42 (greater 2.25 calculated needed render result null) , expected, point estimate exposure crossed null (now less 1).","code":"tip(effect_observed = 1.5, exposure_confounder_effect = 0.5) ## ℹ The observed effect (1.5) WOULD be tipped by 1 unmeasured confounder with the ##   following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.5 ## • estimated relationship between the unmeasured confounder and the outcome: ##   2.25  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1             1.5                     0.5                   2.25 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl> tip(effect_observed = 1.09, exposure_confounder_effect = 0.5) ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.5 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.19  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                     0.5                   1.19 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl> exdata_rr |>   dplyr::group_by(exposure) |>   dplyr::summarise(m = mean(.unmeasured_confounder / sd(.unmeasured_confounder))) |>   tidyr::pivot_wider(names_from = exposure,               values_from = m,               names_prefix = \"u_\") |>   dplyr::summarise(estimate = u_1 - u_0) ## # A tibble: 1 × 1 ##   estimate ##      <dbl> ## 1    0.494 mod_true <- glm(   outcome ~ exposure + measured_confounder + .unmeasured_confounder,    data = exdata_rr,    family = poisson)  mod_true |>   broom::tidy(exponentiate = TRUE, conf.int = TRUE) ## # A tibble: 4 × 7 ##   term                 estimate std.error statistic   p.value conf.low conf.high ##   <chr>                   <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> ## 1 (Intercept)            0.0245    0.163    -22.7   1.49e-114   0.0176    0.0334 ## 2 exposure               0.921     0.172     -0.477 6.34e-  1   0.660     1.30   ## 3 measured_confounder    2.44      0.0746    11.9   6.95e- 33   2.11      2.82   ## 4 .unmeasured_confoun…   2.42      0.0742    11.9   1.35e- 32   2.09      2.80"},{"path":"/index.html","id":"binary-unmeasured-confounder-example","dir":"","previous_headings":"","what":"Binary unmeasured confounder example","title":"Tipping Point Analyses","text":"Now interested binary unmeasured confounder, use tip_with_binary() function. Let’s assume unmeasured confounder prevalent 25% exposed population (exposed_confounder_prev = 0.25) 10% unexposed population (unexposed_confounder_prev = 0.10) – let’s solve relationship unmeasured confounder outcome needed tip analysis (confounder_outcome_effect). hypothetical unobserved binary confounder prevalent 10% unexposed population 25% exposed population need relationship outcome 1.64 tip analysis 5% level, rendering inconclusive.","code":"tip_with_binary(effect_observed = 1.09,                  exposed_confounder_prev = 0.25,                  unexposed_confounder_prev = 0.10) ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated prevalence of the unmeasured confounder in the exposed population: ##   0.25 ## • estimated prevalence of the unmeasured confounder in the unexposed ##   population: 0.1 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.64  ## # A tibble: 1 × 6 ##   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                    0.25                    0.1 ## # ℹ abbreviated name: ¹​unexposed_confounder_prev ## # ℹ 2 more variables: confounder_outcome_effect <dbl>, ## #   n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"many-unmeasured-confounders","dir":"","previous_headings":"","what":"Many unmeasured confounders","title":"Tipping Point Analyses","text":"Suppose concerned many small, independent, continuous, unmeasured confounders present. take 7 independent standardized Normal unmeasured confounders mean difference exposure groups 0.25 relationship outcome 1.05 tip observed analysis 5% level, rendering inconclusive.","code":"tip(effect_observed = 1.09,      exposure_confounder_effect = 0.25,      confounder_outcome_effect = 1.05) ## ℹ The observed effect (1.09) WOULD be tipped by 7 unmeasured confounders with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.25 ## • estimated relationship between the unmeasured confounder and the outcome: ##   1.05  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                    0.25                   1.05 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"integration-with-broom","dir":"","previous_headings":"","what":"Integration with broom","title":"Tipping Point Analyses","text":"functions created easily integrate models tidied using broom package. necessary use functions, nice feature choose . example logistic regression fit glm tidied tidy function broom can directly fed tip() function.","code":"if (requireNamespace(\"broom\", quietly = TRUE) &&  requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(outcome ~ exposure + measured_confounder, data = exdata_rr,             family = poisson) |>     broom::tidy(conf.int = TRUE, exponentiate = TRUE) |>     dplyr::filter(term == \"exposure\") |>     dplyr::pull(conf.low) |>     tip(confounder_outcome_effect = 2.5) } ## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with ##   the following specifications: ## • estimated difference in scaled means between the unmeasured confounder in the ##   exposed population and unexposed population: 0.09 ## • estimated relationship between the unmeasured confounder and the outcome: 2.5  ## # A tibble: 1 × 5 ##   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² ##             <dbl>           <dbl>                   <dbl>                  <dbl> ## 1               1            1.09                  0.0907                    2.5 ## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect ## # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Tipping Point Analyses","text":"Please note tipr project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/reference/adjust_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"Adjust observed regression coefficient normally distributed confounder","code":""},{"path":"/reference/adjust_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"","code":"adjust_coef(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )  adjust_coef_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"Data frame.","code":""},{"path":"/reference/adjust_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed regression coefficient for a normally distributed\nconfounder — adjust_coef","text":"","code":"## Update an observed coefficient of 0.5 with an unmeasured confounder ## with a difference in scaled means between exposure groups of 0.2 ## and coefficient of 0.3 adjust_coef(0.5, 0.2, 0.3) #> ℹ The observed effect (0.5) is updated to 0.44 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.2 #> • estimated relationship between the unmeasured confounder and the outcome: 0.3 #> # A tibble: 1 × 4 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1            0.44             0.5                     0.2                    0.3 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect"},{"path":"/reference/adjust_coef_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"Adjust observed coefficient regression model binary confounder","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"","code":"adjust_coef_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   loglinear = FALSE,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_coef_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"effect_observed Numeric. Observed exposure - outcome effect loglinear model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. loglinear Logical. Calculate adjusted coefficient loglinear model instead linear model (default). loglinear = FALSE, adjust_coef_with_binary() equivalent adjust_coef() exposure_confounder_effect difference prevalences. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_coef_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed coefficient from a regression model with a binary\nconfounder — adjust_coef_with_binary","text":"","code":"adjust_coef_with_binary(1.1, 0.5, 0.3, 1.3) #> ℹ The observed effect (1.1) is updated to 0.84 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.3 #> • estimated relationship between the unmeasured confounder and the outcome: 1.3 #> # A tibble: 1 × 4 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1            0.84             1.1                     0.2                    1.3 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect"},{"path":"/reference/adjust_coef_with_r2.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"function wraps sensemakr::adjusted_estimate() sensemakr::adjusted_se() functions.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"","code":"adjust_coef_with_r2(   effect_observed,   se,   df,   confounder_exposure_r2,   confounder_outcome_r2,   verbose = getOption(\"tipr.verbose\", TRUE),   alpha = 0.05,   ... )"},{"path":"/reference/adjust_coef_with_r2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. confounder_exposure_r2 Numeric value 0 1. assumed partial R2 unobserved confounder exposure given measured covariates. confounder_outcome_r2 Numeric value 0 1. assumed partial R2 unobserved confounder outcome given exposure measured covariates. verbose Logical. Indicates whether print informative message. Default: TRUE alpha Significance level. Default = 0.05. ... Optional arguments passed sensemakr::adjusted_estimate() function.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"data frame.","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"Carlos Cinelli, Jeremy Ferwerda Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr","code":""},{"path":"/reference/adjust_coef_with_r2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — adjust_coef_with_r2","text":"","code":"adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1) #> # A tibble: 1 × 10 #>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed #>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl> #> 1           0.427       0.233       0.621             0.5       0.302 #> # ℹ 5 more variables: ub_observed <dbl>, se_observed <dbl>, df_observed <dbl>, #> #   confounder_exposure_r2 <dbl>, confounder_outcome_r2 <dbl>"},{"path":"/reference/adjust_hr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"Adjust observed hazard ratio normally distributed confounder","code":""},{"path":"/reference/adjust_hr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"","code":"adjust_hr(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )  adjust_hr_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/adjust_hr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_hr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"Data frame.","code":""},{"path":"/reference/adjust_hr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed hazard ratio for a normally distributed\nconfounder — adjust_hr","text":"","code":"adjust_hr(0.9, -0.9, 1.3) #> ℹ The observed effect (HR: 0.9) is updated to HR: 1.14 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: -0.9 #> • estimated relationship (HR) between the unmeasured confounder and the #>   outcome: 1.3 #> # A tibble: 1 × 4 #>   hr_adjusted hr_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1        1.14         0.9                       -0.9                       1.3"},{"path":"/reference/adjust_hr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"Adjust observed hazard ratio binary confounder","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"","code":"adjust_hr_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/adjust_hr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_hr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed hazard ratio with a binary confounder — adjust_hr_with_binary","text":"","code":"adjust_hr_with_binary(0.8, 0.1, 0.5, 1.8) #> ℹ The observed effect (HR: 0.8) is updated to HR: 1.04 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.5 #> • estimated relationship between the unmeasured confounder and the outcome: 1.8 #> # A tibble: 1 × 5 #>   hr_adjusted hr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1        1.04         0.8                     0.1                       0.5 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/adjust_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"Adjust observed odds ratio normally distributed confounder","code":""},{"path":"/reference/adjust_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"","code":"adjust_or(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )  adjust_or_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/adjust_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"Data frame.","code":""},{"path":"/reference/adjust_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed odds ratio for a normally distributed\nconfounder — adjust_or","text":"","code":"adjust_or(1.2, 0.9, 1.3) #> ℹ The observed effect (OR: 1.2) is updated to OR: 0.95 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.9 #> • estimated relationship (OR) between the unmeasured confounder and the #>   outcome: 1.3 #> # A tibble: 1 × 4 #>   or_adjusted or_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1       0.948         1.2                        0.9                       1.3"},{"path":"/reference/adjust_or_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"Adjust observed odds ratio binary confounder","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"","code":"adjust_or_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/adjust_or_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_or_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed odds ratio with a binary confounder — adjust_or_with_binary","text":"","code":"adjust_or_with_binary(3, 1, 0, 3) #> • The observed effect (OR: 3) is updated to OR: 1 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 3 #> # A tibble: 1 × 5 #>   or_adjusted or_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1           1           3                       1                         0 #> # ℹ 1 more variable: confounder_outcome_effect <dbl> adjust_or_with_binary(3, 1, 0, 3, or_correction = TRUE) #> • The observed effect (RR: 1.73) is updated to RR: 1 by a confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.73 #> ℹ You opted to use the odds ratio correction to convert your odds ratios to #>   approximate risk ratios. This is a good idea if the outcome is common (>15%) #> # A tibble: 1 × 5 #>   rr_adjusted rr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1           1        1.73                       1                         0 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/adjust_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"Adjust observed risk ratio normally distributed confounder","code":""},{"path":"/reference/adjust_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"","code":"adjust_rr(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = TRUE )  adjust_rr_with_continuous(   effect_observed,   exposure_confounder_effect,   confounder_outcome_effect,   verbose = TRUE )"},{"path":"/reference/adjust_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric. Estimated relationship unmeasured confounder outcome. verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"Data frame.","code":""},{"path":"/reference/adjust_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed risk ratio for a normally distributed\nconfounder — adjust_rr","text":"","code":"adjust_rr(1.2, 0.5, 1.1) #> ℹ The observed effect (RR: 1.2) is updated to RR: 1.14 by a confounder with the #>   following specifications: #> • estimated difference in scaled means: 0.5 #> • estimated relationship (RR) between the unmeasured confounder and the #>   outcome: 1.1 #> # A tibble: 1 × 4 #>   rr_adjusted rr_observed exposure_confounder_effect confounder_outcome_effect #>         <dbl>       <dbl>                      <dbl>                     <dbl> #> 1        1.14         1.2                        0.5                       1.1"},{"path":"/reference/adjust_rr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"Adjust observed risk ratio binary confounder","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"","code":"adjust_rr_with_binary(   effect_observed,   exposed_confounder_prev,   unexposed_confounder_prev,   confounder_outcome_effect,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/adjust_rr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"Data frame.","code":""},{"path":"/reference/adjust_rr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust an observed risk ratio with a binary confounder — adjust_rr_with_binary","text":"","code":"adjust_rr_with_binary(1.1, 0.5, 0.3, 1.3) #> The observed effect (1.1) is updated to 1.04 by a confounder with the following #> specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.3 #> • estimated relationship between the unmeasured confounder and the outcome: 1.3 #> # A tibble: 1 × 5 #>   rr_adjusted rr_observed exposed_confounder_prev unexposed_confounder_prev #>         <dbl>       <dbl>                   <dbl>                     <dbl> #> 1        1.04         1.1                     0.5                       0.3 #> # ℹ 1 more variable: confounder_outcome_effect <dbl>"},{"path":"/reference/e_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate an E-value — e_value","title":"Calculate an E-value — e_value","text":"Calculate E-value","code":""},{"path":"/reference/e_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate an E-value — e_value","text":"","code":"e_value(effect_observed)"},{"path":"/reference/e_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate an E-value — e_value","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound.","code":""},{"path":"/reference/e_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate an E-value — e_value","text":"Numeric value","code":""},{"path":"/reference/e_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate an E-value — e_value","text":"","code":"e_value(0.9) #> [1] 1.462475 e_value(1.3) #> [1] 1.9245"},{"path":"/reference/exdata_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Example Data (Continuous Outcome) — exdata_continuous","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"data set simulated two Normally distributed confounders, one \"measured\" one \"unmeasured\", exposure, outcome. \"true\" causal effect exposure outcome, accounting measured unmeasured confounders, 0.","code":""},{"path":"/reference/exdata_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"","code":"exdata_continuous"},{"path":"/reference/exdata_continuous.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example Data (Continuous Outcome) — exdata_continuous","text":"data frame 2,000 rows 4 columns: .unmeasured_confounder: simulated unmeasured confounder measured_confounder: simulated measured confounder exposure outcome","code":""},{"path":"/reference/exdata_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Example Data (Risk Ratio) — exdata_rr","title":"Example Data (Risk Ratio) — exdata_rr","text":"data set simulated two Normally distributed confounders, one \"measured\" one \"unmeasured\", exposure, outcome. \"true\" causal effect exposure outcome, accounting measured unmeasured confounders, 0.","code":""},{"path":"/reference/exdata_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example Data (Risk Ratio) — exdata_rr","text":"","code":"exdata_rr"},{"path":"/reference/exdata_rr.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example Data (Risk Ratio) — exdata_rr","text":"data frame 2,000 rows 4 columns: .unmeasured_confounder: simulated unmeasured confounder measured_confounder: simulated measured confounder exposure outcome","code":""},{"path":"/reference/observed_bias_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Order observed bias data frame for plotting — observed_bias_order","title":"Order observed bias data frame for plotting — observed_bias_order","text":"Order observed bias data frame plotting","code":""},{"path":"/reference/observed_bias_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Order observed bias data frame for plotting — observed_bias_order","text":"","code":"observed_bias_order(d, by)"},{"path":"/reference/observed_bias_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Order observed bias data frame for plotting — observed_bias_order","text":"d Observed bias data frame. Must columns dropped type Character. Variable d order .","code":""},{"path":"/reference/observed_bias_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Order observed bias data frame for plotting — observed_bias_order","text":"Data frame correct order","code":""},{"path":"/reference/observed_bias_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"Create data frame assist creating observed bias plot","code":""},{"path":"/reference/observed_bias_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"","code":"observed_bias_tbl(ps_mod, outcome_mod, drop_list = NULL)"},{"path":"/reference/observed_bias_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"ps_mod Model object propensity score model outcome_mod Model object outcome model drop_list Named list covariates groups covariates drop NULL, default dropping covariate one time.","code":""},{"path":"/reference/observed_bias_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"Data frame following columns: dropped: covariate group covariates dropped type: Explanation dropped, whether refers single covariate (covariate) group covariates (group) ps_formula: new formula updated propensity score model outcome_formula: new formula updated outcome model ps_model: new model object updated propensity score model p: updated propensity score","code":""},{"path":"/reference/observed_bias_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a data frame to assist with creating an observed bias plot — observed_bias_tbl","text":"","code":"ps_mod <- glm(am ~ mpg + cyl + I(hp^2), data = mtcars) outcome_mod <- lm(qsec ~ am + hp + disp + wt, data = mtcars) observed_bias_tbl(  ps_mod,  outcome_mod,  drop_list = list(    group_one = c(\"mpg\", \"hp\"),    group_two = c(\"cyl\", \"wt\")    )  ) #> # A tibble: 2 × 6 #>   dropped   type  ps_formula outcome_formula ps_model p          #>   <chr>     <chr> <list>     <list>          <list>   <list>     #> 1 group_one group <formula>  <formula>       <glm>    <dbl [32]> #> 2 group_two group <formula>  <formula>       <glm>    <dbl [32]>"},{"path":"/reference/observed_bias_tip.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"Create data frame combine observed bias data frame demonstrating hypothetical unmeasured confounder","code":""},{"path":"/reference/observed_bias_tip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"","code":"observed_bias_tip(   tip,   point_estimate,   lb,   ub,   tip_desc = \"Hypothetical unmeasured confounder\" )"},{"path":"/reference/observed_bias_tip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"tip Numeric. Value like tip . point_estimate Numeric. Result estimate full model. lb Numeric. Result lower bound full model. ub Numeric. Result upper bound full model. tip_desc Character. description tipping point.","code":""},{"path":"/reference/observed_bias_tip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder — observed_bias_tip","text":"data frame five columns: dropped: input tip_desc type: Explanation dropped, tip clarify calculated tipping point. point_estimate: shifted point estimate lb: shifted lower bound ub: shifted upper bound","code":""},{"path":"/reference/observed_covariate_e_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Observed Covariate E-value — observed_covariate_e_value","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"Calculate Observed Covariate E-value","code":""},{"path":"/reference/observed_covariate_e_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"","code":"observed_covariate_e_value(lb, ub, lb_adj, ub_adj, transform = NULL)"},{"path":"/reference/observed_covariate_e_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"lb Numeric. lower bound full model ub Numeric. upper bound full model lb_adj Numeric. lower bound adjusted model ub_adj Numeric. upper bound adjusted model transform Character. effect odds ratio hazard ratio, perform transformation suggested VanderWeele Ding. Allowed values : \"\" \"HR\"","code":""},{"path":"/reference/observed_covariate_e_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the Observed Covariate E-value — observed_covariate_e_value","text":"Observed Covariate E-value","code":""},{"path":"/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"Pipe operator","code":""},{"path":"/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"/reference/r_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Robustness value — r_value","title":"Robustness value — r_value","text":"function wraps sensemakr::robustness_value() function","code":""},{"path":"/reference/r_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Robustness value — r_value","text":"","code":"r_value(effect_observed, se, df, ...)"},{"path":"/reference/r_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Robustness value — r_value","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. ... Optional arguments passed sensemakr::robustness_value() function.","code":""},{"path":"/reference/r_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Robustness value — r_value","text":"Numeric. Robustness value","code":""},{"path":"/reference/r_value.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Robustness value — r_value","text":"Carlos Cinelli, Jeremy Ferwerda Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr","code":""},{"path":"/reference/r_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Robustness value — r_value","text":"","code":"r_value(0.5, 0.1, 102) #> [1] 0.387467"},{"path":"/reference/tip.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a result with a normally distributed confounder. — tip","title":"Tip a result with a normally distributed confounder. — tip","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a result with a normally distributed confounder. — tip","text":"","code":"tip(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_c(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )"},{"path":"/reference/tip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a result with a normally distributed confounder. — tip","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE correction_factor Character string. Options \"none\", \"hr\", \"\". common outcomes (>15%), odds ratio hazard ratio good estimate risk ratio. cases, can apply correction factor. supplying hazard ratio common outcome, set \"hr\"; supplying odds ratio common outcome, set \"\"; supplying risk ratio outcome rare, set \"none\" (default).","code":""},{"path":"/reference/tip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a result with a normally distributed confounder. — tip","text":"Data frame.","code":""},{"path":"/reference/tip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a result with a normally distributed confounder. — tip","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip(confounder_outcome_effect = 2.5) } #> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: 0.13 #> • estimated relationship between the unmeasured confounder and the outcome: 2.5 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.13                   0.133                    2.5 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a linear model coefficient with a continuous confounder. — tip_coef","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"","code":"tip_coef(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )  tip_coef_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. can beta coefficient, lower confidence bound beta coefficient, upper confidence bound beta coefficient. exposure_confounder_effect Numeric. Estimated scaled mean difference unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"Data frame.","code":""},{"path":"/reference/tip_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a linear model coefficient with a continuous confounder. — tip_coef","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_coef(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   -0.6 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1             1.2                         -2                      -0.6 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_coef(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = -0.05) #> ℹ The observed effect (1.2) WOULD be tipped by 12 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   -0.05 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1             1.2                         -2                     -0.05 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   lm(wt ~ mpg, data = mtcars) %>%    broom::tidy(conf.int = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_coef(confounder_outcome_effect = 2.5) } #> ℹ The observed effect (-0.17) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -0.07 #> • estimated relationship between the unmeasured confounder and the outcome: 2.5 #> # A tibble: 1 × 4 #>   effect_observed exposure_confounder_effect confounder_outcome_effect #>             <dbl>                      <dbl>                     <dbl> #> 1          -0.171                    -0.0684                       2.5 #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_coef_with_r2.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"Choose one following, estimated: confounder_exposure_r2 confounder_outcome_r2","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"","code":"tip_coef_with_r2(   effect_observed,   se,   df,   confounder_exposure_r2 = NULL,   confounder_outcome_r2 = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   alpha = 0.05,   tip_bound = FALSE,   ... )"},{"path":"/reference/tip_coef_with_r2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"effect_observed Numeric. Observed exposure - outcome effect regression model. point estimate (beta coefficient) se Numeric. Standard error effect_observed previous parameter. df Numeric positive value. Residual degrees freedom model used estimate observed exposure - outcome effect. total number observations minus number parameters estimated model. Often models estimated intercept N - k - 1 k number predictors model. confounder_exposure_r2 Numeric value 0 1. assumed partial R2 unobserved confounder exposure given measured covariates. confounder_outcome_r2 Numeric value 0 1. assumed partial R2 unobserved confounder outcome given exposure measured covariates. verbose Logical. Indicates whether print informative message. Default: TRUE alpha Significance level. Default = 0.05. tip_bound want tip bound? Default = FALSE, tip point estimate ... Optional arguments passed sensemakr::adjusted_estimate() function.","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"data frame.","code":""},{"path":"/reference/tip_coef_with_r2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a regression coefficient using the partial R2\nfor an unmeasured confounder-exposure relationship and unmeasured confounder-\noutcome relationship — tip_coef_with_r2","text":"","code":"tip_coef_with_r2(0.5, 0.1, 102, 0.5) #> # A tibble: 1 × 10 #>   effect_adjusted lb_adjusted ub_adjusted effect_observed lb_observed #>             <dbl>       <dbl>       <dbl>           <dbl>       <dbl> #> 1        5.55e-17      -0.245       0.245             0.5       0.302 #> # ℹ 5 more variables: ub_observed <dbl>, se_observed <dbl>, df_observed <dbl>, #> #   confounder_exposure_r2 <dbl>, confounder_outcome_r2 <dbl>"},{"path":"/reference/tip_hr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_hr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"","code":"tip_hr(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )  tip_hr_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/tip_hr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_hr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"Data frame.","code":""},{"path":"/reference/tip_hr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed hazard ratio with a normally distributed confounder. — tip_hr","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_hr(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_hr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_hr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"","code":"tip_hr_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   hr_correction = FALSE )"},{"path":"/reference/tip_hr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome hazard ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE hr_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, hazard ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"Data frame.","code":""},{"path":"/reference/tip_hr_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed hazard ratio with a binary confounder. — tip_hr_with_binary","text":"","code":"tip_hr_with_binary(0.9, 0.9, 0.1) #> ℹ The observed effect (0.9) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.9 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.1 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.88 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             0.9                     0.9                    0.1 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"","code":"tip_or(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )  tip_or_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/tip_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"Data frame.","code":""},{"path":"/reference/tip_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed odds ratio with a normally distributed confounder. — tip_or","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_or(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_or(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_or(confounder_outcome_effect = 2.5, or_correction = TRUE) } #> ℹ The observed effect (1.06) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: 0.13 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.58 #> ℹ You opted to use the odds ratio correction to convert your odds ratios to #>   approximate risk ratios. This is a good idea if the outcome is common (>15%). #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.06                   0.133                   1.58 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_or_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"","code":"tip_or_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   or_correction = FALSE )"},{"path":"/reference/tip_or_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome odds ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE or_correction Logical. Indicates whether use correction factor. methods used function based risk ratios. rare outcomes, odds ratio approximates risk ratio. common outcomes, correction factor needed. common outcome (>15%), set TRUE. Default: FALSE.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"Data frame.","code":""},{"path":"/reference/tip_or_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed odds ratio with a binary confounder. — tip_or_with_binary","text":"","code":"tip_or_with_binary(0.9, 0.9, 0.1) #> ℹ The observed effect (0.9) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.9 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.1 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.88 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             0.9                     0.9                    0.1 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_rr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"choose one following, estimated: exposure_confounder_effect confounder_outcome_effect","code":""},{"path":"/reference/tip_rr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"","code":"tip_rr(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )  tip_rr_with_continuous(   effect_observed,   exposure_confounder_effect = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_rr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposure_confounder_effect Numeric. Estimated difference scaled means unmeasured confounder exposed population unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_rr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"Data frame.","code":""},{"path":"/reference/tip_rr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip an observed risk ratio with a normally distributed confounder. — tip_rr","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_rr(1.2, exposure_confounder_effect = -2) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.91 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                  0.913 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_rr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99) #> ℹ The observed effect (1.2) WOULD be tipped by 9 unmeasured confounders with #>   the following specifications: #> • estimated difference in scaled means between the unmeasured confounder in the #>   exposed population and unexposed population: -2 #> • estimated relationship between the unmeasured confounder and the outcome: #>   0.99 #> # A tibble: 1 × 5 #>   effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…² #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                      -2                   0.99 #> # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect #> # ℹ 1 more variable: n_unmeasured_confounders <dbl>"},{"path":"/reference/tip_rr_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_rr_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"","code":"tip_rr_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE) )"},{"path":"/reference/tip_rr_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome risk ratio. can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE","code":""},{"path":"/reference/tip_with_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Tip a result with a binary confounder. — tip_with_binary","title":"Tip a result with a binary confounder. — tip_with_binary","text":"Choose two following three specify, third estimated: exposed_confounder_prev unexposed_confounder_prev confounder_outcome_effect Alternatively, specify three function return number unmeasured confounders specified needed tip analysis.","code":""},{"path":"/reference/tip_with_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tip a result with a binary confounder. — tip_with_binary","text":"","code":"tip_with_binary(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )  tip_b(   effect_observed,   exposed_confounder_prev = NULL,   unexposed_confounder_prev = NULL,   confounder_outcome_effect = NULL,   verbose = getOption(\"tipr.verbose\", TRUE),   correction_factor = \"none\" )"},{"path":"/reference/tip_with_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tip a result with a binary confounder. — tip_with_binary","text":"effect_observed Numeric positive value. Observed exposure - outcome effect (assumed exponentiated coefficient, risk ratio, odds ratio, hazard ratio). can point estimate, lower confidence bound, upper confidence bound. exposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder exposed population unexposed_confounder_prev Numeric 0 1. Estimated prevalence unmeasured confounder unexposed population confounder_outcome_effect Numeric positive value. Estimated relationship unmeasured confounder outcome verbose Logical. Indicates whether print informative message. Default: TRUE correction_factor Character string. Options \"none\", \"hr\", \"\". common outcomes (>15%), odds ratio hazard ratio good estimate risk ratio. cases, can apply correction factor. supplying hazard ratio common outcome, set \"hr\"; supplying odds ratio common outcome, set \"\"; supplying risk ratio outcome rare, set \"none\" (default).","code":""},{"path":"/reference/tip_with_binary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tip a result with a binary confounder. — tip_with_binary","text":"tip_b() alias tip_with_binary().","code":""},{"path":"/reference/tip_with_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tip a result with a binary confounder. — tip_with_binary","text":"","code":"## to estimate the relationship between an unmeasured confounder and outcome ## needed to tip analysis tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0) #> ℹ The observed effect (1.2) WOULD be tipped by 1 unmeasured confounder with the #>   following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 1.4 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                     0.5                      0 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>  ## to estimate the number of unmeasured confounders specified needed to tip ## the analysis tip_with_binary(1.2,   exposed_confounder_prev = 0.5,   unexposed_confounder_prev = 0,   confounder_outcome_effect = 1.1) #> ℹ The observed effect (1.2) WOULD be tipped by 4 unmeasured confounders with #>   the following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   0.5 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0 #> • estimated relationship between the unmeasured confounder and the outcome: 1.1 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1             1.2                     0.5                      0 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>  ## Example with broom if (requireNamespace(\"broom\", quietly = TRUE) &&     requireNamespace(\"dplyr\", quietly = TRUE)) {   glm(am ~ mpg, data = mtcars, family = \"binomial\") %>%    broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%    dplyr::filter(term == \"mpg\") %>%    dplyr::pull(conf.low) %>%    tip_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15) } #> ℹ The observed effect (1.13) WOULD be tipped by 1 unmeasured confounder with #>   the following specifications: #> • estimated prevalence of the unmeasured confounder in the exposed population: #>   1 #> • estimated prevalence of the unmeasured confounder in the unexposed #>   population: 0.12 #> • estimated relationship between the unmeasured confounder and the outcome: #>   1.15 #> # A tibble: 1 × 6 #>   effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹ #>             <dbl>           <dbl>                   <dbl>                  <dbl> #> 1               1            1.13                       1                  0.119 #> # ℹ abbreviated name: ¹​unexposed_confounder_prev #> # ℹ 2 more variables: confounder_outcome_effect <dbl>, #> #   n_unmeasured_confounders <dbl>"},{"path":"/reference/tipr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tipr — tipr-package","title":"tipr — tipr-package","text":"tipr package.","code":""},{"path":"/reference/tipr-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tipr — tipr-package","text":"D'Agostino McGowan, L, (2022). tipr: R package sensitivity analyses unmeasured confounders. Journal Open Source Software, 7(77), 4495. D’Agostino McGowan, L. (2022). Sensitivity Analyses Unmeasured Confounders. Current Epidemiology Reports, 9(4), 361-375. VanderWeele, TJ, Peng D (2017). Sensitivity Analysis Observational Research: Introducing E-Value. Ann Intern Med, 167(4), 268–74. Cinelli, C, & Hazlett, C (2020). Making sense sensitivity: Extending omitted variable bias. Journal Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39–67. Lin, DY, Psaty, BM, & Kronmal, RA. (1998). Assessing sensitivity regression results unmeasured confounders observational studies. Biometrics, 54(3), 948–963.","code":""},{"path":[]},{"path":"/reference/tipr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tipr — tipr-package","text":"Maintainer: Lucy D'Agostino McGowan lucydagostino@gmail.com (ORCID) contributors: Malcolm Barrett malcolmbarrett@gmail.com (ORCID) [contributor]","code":""},{"path":"/news/index.html","id":"tipr-102","dir":"Changelog","previous_headings":"","what":"tipr 1.0.2","title":"tipr 1.0.2","text":"adjust_coef_with_binary() now assumes coefficient linear model rather loglinear. Use loglinear = TRUE get old behavior. (#12, @malcolmbarrett) Fixed roxygen issue package documentation","code":""},{"path":"/news/index.html","id":"tipr-101","dir":"Changelog","previous_headings":"","what":"tipr 1.0.1","title":"tipr 1.0.1","text":"CRAN release: 2022-09-05 Fixed bug, functions based adjust_coef_with_binary function old parameter names (exposed_p unexposed_p). changed match new updates version 1.0.0 now exposed_confounder_prev unexposed_confounder_prev. Change “relative risk” “risk ratio” documentation. Add new JOSS citation","code":""},{"path":"/news/index.html","id":"tipr-100","dir":"Changelog","previous_headings":"","what":"tipr 1.0.0","title":"tipr 1.0.0","text":"CRAN release: 2022-08-06 Breaking changes. names several arguments changed increased clarity: effect -> effect_observed outcome_association -> confounder_outcome_effect smd -> exposure_confounder_effect exposed_p -> exposed_confounder_prev unexposed_p -> unexposed_confounder_prev exposure_r2 -> confounder_exposure_r2 outcome_r2 -> confounder_outcome_r2 Added two new example datasets: exdata_continuous exdata_rr","code":""},{"path":"/news/index.html","id":"tipr-042","dir":"Changelog","previous_headings":"","what":"tipr 0.4.2","title":"tipr 0.4.2","text":"Make output tibble names consistent (adjusted_effect -> effect_adjusted)","code":""},{"path":"/news/index.html","id":"tipr-041","dir":"Changelog","previous_headings":"","what":"tipr 0.4.1","title":"tipr 0.4.1","text":"CRAN release: 2022-05-05 Add additional functions specify *_with_continuous() (long form , function names, default unmeasured confounder Normally distributed) Change tip_lm() tip_coef().","code":""},{"path":"/news/index.html","id":"tipr-040","dir":"Changelog","previous_headings":"","what":"tipr 0.4.0","title":"tipr 0.4.0","text":"CRAN release: 2022-04-16 Changed name lm_tip() tip_lm() API fundamentally updated functions now take numeric value first argument rather data frame. Added adjust_* functions allow specification unmeasured confounder qualities without tipping Split tip_* functions hazard ratio, odds ratio, relative risk Add R2 parameterization tip_coef_with_r2(), adjust_coef_with_r2(), r_value()","code":""},{"path":"/news/index.html","id":"tipr-030","dir":"Changelog","previous_headings":"","what":"tipr 0.3.0","title":"tipr 0.3.0","text":"CRAN release: 2021-09-10 Added ability perform sensitivity analyses linear models via lm_tip()","code":""},{"path":"/news/index.html","id":"tipr-020","dir":"Changelog","previous_headings":"","what":"tipr 0.2.0","title":"tipr 0.2.0","text":"CRAN release: 2020-11-16 Updated several function parameter names. main functions now tip() tip_with_binary(). parameter names self-explanatory. API fundamentally updated functions now take data frame first argument. now explicit (required) integration broom package.","code":""},{"path":"/news/index.html","id":"tipr-011","dir":"Changelog","previous_headings":"","what":"tipr 0.1.1","title":"tipr 0.1.1","text":"CRAN release: 2017-11-28 initial CRAN release","code":""}]