v0.7.2 update

Author: pzivich

CHANGELOG.md

@@ -1,5 +1,10 @@
 ## Change logs
 
+### v0.7.2
+Labeling fix for `RiskDifference` summary
+
+Adding option to extract standard errors from `TMLE` and `AIPTW`
+
 ### v0.7.1
 Warning for upcoming change for `IPTW` in v0.8.0. To better align with other causal estimators, `IPTW` will no longer 
 only return a vector of weights. Behind the scenes, `IPTW` will be able to estimate the marginal structural model 

zepid/base.py

@@ -411,7 +411,7 @@ def summary(self, decimal=3):
             print(tabulate([['E=1', a, b], ['E=0', self._c, self._d]], headers=['', 'D=1', 'D=0'],
                            tablefmt='grid'), '\n')
         print('======================================================================')
-        print('                            Risk Ratio                                ')
+        print('                         Risk Difference                              ')
         print('======================================================================')
         print(self.results[['Risk', 'SD(Risk)', 'Risk_LCL', 'Risk_UCL']].round(decimals=decimal))
         print('----------------------------------------------------------------------')

zepid/causal/doublyrobust/AIPW.py

@@ -114,9 +114,11 @@ def __init__(self, df, exposure, outcome, weights=None, alpha=0.05):
         self.risk_ratio = None
         self.risk_difference_ci = None
         self.risk_ratio_ci = None
+        self.risk_difference_se = None
 
         self.average_treatment_effect = None
         self.average_treatment_effect_ci = None
+        self.average_treatment_effect_se = None
 
         self._fit_exposure_ = False
         self._fit_outcome_ = False
@@ -225,14 +227,15 @@ def fit(self):
             if self._continuous_outcome:
                 self.average_treatment_effect = np.mean(dr_a1) - np.mean(dr_a0)
                 var_ic = np.var((dr_a1 - dr_a0) - self.average_treatment_effect, ddof=1) / self.df.shape[0]
+                self.average_treatment_effect_se = np.sqrt(var_ic)
                 self.average_treatment_effect_ci = [self.average_treatment_effect - zalpha * np.sqrt(var_ic),
                                                     self.average_treatment_effect + zalpha * np.sqrt(var_ic)]
 
             else:
                 self.risk_difference = np.mean(dr_a1) - np.mean(dr_a0)
                 self.risk_ratio = np.mean(dr_a1) / np.mean(dr_a0)
-
                 var_ic = np.var((dr_a1 - dr_a0) - self.risk_difference, ddof=1) / self.df.shape[0]
+                self.risk_difference_se = np.sqrt(var_ic)
                 self.risk_difference_ci = [self.risk_difference - zalpha * np.sqrt(var_ic),
                                            self.risk_difference + zalpha * np.sqrt(var_ic)]
         else:

zepid/causal/doublyrobust/TMLE.py

@@ -185,12 +185,16 @@ def __init__(self, df, exposure, outcome, alpha=0.05, continuous_bound=0.0005):
 
         self.risk_difference = None
         self.risk_difference_ci = None
+        self.risk_difference_se = None
         self.risk_ratio = None
         self.risk_ratio_ci = None
+        self.risk_ratio_se = None
         self.odds_ratio = None
         self.odds_ratio_ci = None
+        self.odds_ratio_se = None
         self.average_treatment_effect = None
         self.average_treatment_effect_ci = None
+        self.average_treatment_effect_se = None
 
     def exposure_model(self, model, custom_model=None, bound=False, print_results=True):
         """Estimation of Pr(A=1|L), which is termed as g(A=1|L) in the literature
@@ -437,19 +441,21 @@ def fit(self):
             ic = np.where(delta == 1,
                           HAW * (y_unbound - Qstar) + (Qstar1 - Qstar0) - self.average_treatment_effect,
                           Qstar1 - Qstar0 - self.average_treatment_effect)
-            varIC = np.nanvar(ic, ddof=1) / self.df.shape[0]
-            self.average_treatment_effect_ci = [self.average_treatment_effect - zalpha * math.sqrt(varIC),
-                                                self.average_treatment_effect + zalpha * math.sqrt(varIC)]
+            seIC = np.sqrt(np.nanvar(ic, ddof=1) / self.df.shape[0])
+            self.average_treatment_effect_se = seIC
+            self.average_treatment_effect_ci = [self.average_treatment_effect - zalpha * seIC,
+                                                self.average_treatment_effect + zalpha * seIC]
         else:
             # Calculating Risk Difference
             self.risk_difference = np.nanmean(Qstar1 - Qstar0)
             # Influence Curve for CL
             ic = np.where(delta == 1,
                           HAW * (self.df[self.outcome] - Qstar) + (Qstar1 - Qstar0) - self.risk_difference,
                           (Qstar1 - Qstar0) - self.risk_difference)
-            varIC = np.nanvar(ic, ddof=1) / self.df.shape[0]
-            self.risk_difference_ci = [self.risk_difference - zalpha * np.sqrt(varIC),
-                                       self.risk_difference + zalpha * np.sqrt(varIC)]
+            seIC = np.sqrt(np.nanvar(ic, ddof=1) / self.df.shape[0])
+            self.risk_difference_se = seIC
+            self.risk_difference_ci = [self.risk_difference - zalpha * seIC,
+                                       self.risk_difference + zalpha * seIC]
 
             # Calculating Risk Ratio
             self.risk_ratio = np.nanmean(Qstar1) / np.nanmean(Qstar0)
@@ -459,9 +465,10 @@ def fit(self):
                            (1/np.mean(Qstar0)) * (-1 * H0W * (self.df[self.outcome] - Qstar) + Qstar0 - np.mean(Qstar0))),
                           (Qstar1 - np.mean(Qstar1)) + Qstar0 - np.mean(Qstar0))
 
-            varIC = np.nanvar(ic, ddof=1) / self.df.shape[0]
-            self.risk_ratio_ci = [np.exp(np.log(self.risk_ratio) - zalpha * np.sqrt(varIC)),
-                                  np.exp(np.log(self.risk_ratio) + zalpha * np.sqrt(varIC))]
+            seIC = np.sqrt(np.nanvar(ic, ddof=1) / self.df.shape[0])
+            self.risk_ratio_se = seIC
+            self.risk_ratio_ci = [np.exp(np.log(self.risk_ratio) - zalpha * seIC),
+                                  np.exp(np.log(self.risk_ratio) + zalpha * seIC)]
 
             # Calculating Odds Ratio
             self.odds_ratio = (np.nanmean(Qstar1) / (1 - np.nanmean(Qstar1)
@@ -475,9 +482,10 @@ def fit(self):
 
                           ((1 / (np.nanmean(Qstar1) * (1 - np.nanmean(Qstar1))) * Qstar1 -
                            (1 / (np.nanmean(Qstar0) * (1 - np.nanmean(Qstar0))) * Qstar0))))
-            varIC = np.nanvar(ic, ddof=1) / self.df.shape[0]
-            self.odds_ratio_ci = [np.exp(np.log(self.odds_ratio) - zalpha * np.sqrt(varIC)),
-                                  np.exp(np.log(self.odds_ratio) + zalpha * np.sqrt(varIC))]
+            seIC = np.sqrt(np.nanvar(ic, ddof=1) / self.df.shape[0])
+            self.odds_ratio_se = seIC
+            self.odds_ratio_ci = [np.exp(np.log(self.odds_ratio) - zalpha * seIC),
+                                  np.exp(np.log(self.odds_ratio) + zalpha * seIC)]
 
     def summary(self, decimal=3):
         """Prints summary of model results

zepid/version.py

@@ -1 +1 @@
-__version__ = '0.7.1'
+__version__ = '0.7.2'