closes #143 Navitas R blog

inst/WORDLIST.txt

@@ -87,6 +87,7 @@ atoxgr
 auth
 AVAL
 AVALU
+Balu
 basel
 bayer
 bb
@@ -102,6 +103,7 @@ bindEvent
 Biostatisticians
 Biostatistics
 Biostats
+Biotechs
 blogpost
 bmi
 BMI
@@ -166,6 +168,7 @@ datetime
 Datetime
 dcf
 DCUTDT
+De
 defaultPageSize
 deliverables
 DEMOADY
@@ -201,6 +204,7 @@ DTHFL
 dtm
 DTM
 dy
+EBGM
 eclinical
 eCTD
 edoardo
@@ -216,6 +220,7 @@ envsetup
 EOSDT
 EPI
 eps
+eSubmission
 ethz
 eval
 eventID
@@ -354,6 +359,7 @@ modularize
 Mosteller
 msrc
 na
+Navitas
 nas
 natively
 ncol
@@ -482,6 +488,7 @@ Siggaard
 signif
 siloed
 SITEIDN
+skillsets
 soumitra
 Soumitra
 ss
@@ -564,6 +571,7 @@ vehIoJgdA
 Velásquez's
 venkatachalam
 Venkatachalam
+Venkatesan
 Veríssimo
 ViiV
 Viyash
@@ -587,6 +595,7 @@ webr
 webR
 WebR
 wfk
+WG
 wg
 wikipedia
 WnpvVgmyE

posts/2024-04-15_de-_mystifying__.../appendix.R

@@ -0,0 +1,78 @@
+# markdown helpers --------------------------------------------------------
+
+markdown_appendix <- function(name, content) {
+  paste(paste("##", name, "{.appendix}"), " ", content, sep = "\n")
+}
+markdown_link <- function(text, path) {
+  paste0("[", text, "](", path, ")")
+}
+
+
+
+# worker functions --------------------------------------------------------
+
+insert_source <- function(repo_spec, name,
+                          collection = "posts",
+                          branch = "main",
+                          host = "https://github.com",
+                          text = "source code") {
+  path <- paste(
+    host,
+    repo_spec,
+    "tree",
+    branch,
+    collection,
+    name,
+    "code_sections.qmd",
+    sep = "/"
+  )
+  return(markdown_link(text, path))
+}
+
+insert_timestamp <- function(tzone = Sys.timezone()) {
+  time <- lubridate::now(tzone = tzone)
+  stamp <- as.character(time, tz = tzone, usetz = TRUE)
+  return(stamp)
+}
+
+insert_lockfile <- function(repo_spec, name,
+                            collection = "posts",
+                            branch = "main",
+                            host = "https://github.com",
+                            text = "R environment") {
+  path <- paste(
+    host,
+    repo_spec,
+    "tree",
+    branch,
+    collection,
+    name,
+    "renv.lock",
+    sep = "/"
+  )
+  return(markdown_link(text, path))
+}
+
+
+
+# top level function ------------------------------------------------------
+
+insert_appendix <- function(repo_spec, name, collection = "posts") {
+  appendices <- paste(
+    markdown_appendix(
+      name = "Last updated",
+      content = insert_timestamp()
+    ),
+    " ",
+    markdown_appendix(
+      name = "Details",
+      content = paste(
+        insert_source(repo_spec, name, collection),
+        insert_lockfile(repo_spec, name, collection),
+        sep = ", "
+      )
+    ),
+    sep = "\n"
+  )
+  knitr::asis_output(appendices)
+}

posts/2024-04-15_de-_mystifying__.../de-_mystifying__r__programming_in__clinical__trials.qmd

@@ -0,0 +1,180 @@
+---
+title: "De-Mystifying R Programming in Clinical Trials"
+author:
+  - name: Venkatesan Balu
+description: "A blog highlighting the benefits/limitations of using R Programming and using the right tools to create value"
+date: "2024-04-15"
+categories: [community]
+image: "Rlogo.png"
+editor: 
+  markdown: 
+    wrap: 72
+---
+
+<!--------------- typical setup ----------------->
+
+```{r setup, include=FALSE}
+long_slug <- "2024-04-15_de-_mystifying__..."
+# renv::use(lockfile = "renv.lock")
+```
+
+<!--------------- post begins here ----------------->
+
+## Introduction
+
+The use of R programming in clinical trials has not always been the most
+popular and obvious in the past. Despite experiencing significant growth
+in recent years, the adoption of R programming in clinical trials is not
+as widespread and evident as anticipated. Practical implementation faces
+obstacles due to various factors, including occasional
+misunderstandings, particularly in the context of validation, and a
+notable lack of awareness regarding its capabilities. However, despite
+these challenges, R is steadily establishing a growing niche within the
+pharmaceutical industry.
+
+## Opportunities for R Programming in Clinical Trials
+
+Although R is versatile and applicable in various settings, it is
+commonly associated with scientific computing and statistics. In the
+context of clinical trials, where researchers aim to understand and
+enhance drug development and testing processes, R has become a prominent
+tool for analyzing the collected data. While SAS® has been a
+longstanding programming language for clinical trials, the industry has
+been exploring alternative options. There is a quest for sustainable
+technology and tools that can effectively address industry challenges.
+
+To drive innovation, there is a need to move away from traditional,
+inefficient processes and tools toward solutions that are efficient,
+simple, easy to implement, reliable, and cost-effective. Collaboration
+among industry stakeholders is crucial to develop a robust technology
+ecosystem and establish consensus on validation and regulatory
+benchmarks. Equally vital is preparing the workforce with the necessary
+skillsets to meet future demands.
+
+## Current Usage Trends of R
+
+Analyzing the current trends of R in the pharmaceutical industry reveals
+that its usage still has room for growth when related to Pharma
+Regulatory Submissions. However, R finds extensive use in public health
+projects, healthcare economics, exploratory and scientific analysis,
+trend identification, generating plots/graphs, specific statistical
+analysis, and machine learning. R continues to advance steadily in
+clinical trials, however lacks widespread usage within the clinical
+space.
+
+This is an area that we see gradually evolving thanks to a number of
+across-industry efforts such as pharmaverse.
+
+## SAS® or R Programming: Which is Better?
+
+![SAS® or R?](Signpost.jpg){fig-align="center" width="500px"}
+
+The ongoing debate in the programming community revolves around whether
+to replace SAS® with R, use both, or explore other alternatives like
+Python. Instead of adopting an either-or scenario, leveraging the
+strengths of each programming language for specific Data Science
+problems is recommended, recognizing that one size does not fit all.
+Early adopters of R have faced challenges, with regulatory compliance
+for R packages being a common issue.
+
+For R to be considered for tasks related to regulatory submission, a
+rigorous risk assessment of R packages, feasibility analysis, and the
+establishment of processes for R usage through pilot projects with
+necessary documentation becomes imperative. We see great progress in
+this area through efforts such as the [R Consortium R Submissions
+WG](https://rconsortium.github.io/submissions-wg/).
+
+## Benefits of Using R Programming
+
+R, as a language and environment for statistical computing and graphics,
+possesses characteristics that make it a potentially powerful tool for
+Data Analysis. With approximately 2 million users worldwide and three
+decades of legacy, R stands out as open-source software receiving
+substantial support from the community. Its availability under the GNU
+General Public License and extensive documentation contribute to its
+strength. R is versatile, running on various platforms, offering a wide
+array of statistical and graphical techniques, and its ease of producing
+publication-quality plots enhances its appeal.
+
+The pharmaceutical industry has witnessed the emergence of various R
+packages tailored for Clinical Trial reporting. Examples include
+`{rtables}` for creating tables for reporting clinical trials,
+`{admiral}` for CDISC ADaM, `{pkglite}` to support eSubmission, and many
+others. These packages cater to different aspects of clinical trial data
+analysis, showcasing the versatility of R in this domain.
+
+This article talks more about use of R in clinical trials and how this
+will be used by taking advantages of open source of R. The FDA
+emphasizes the need for fully documenting software packages used for
+statistical analysis in submissions. The use of R poses specific
+challenges related to validation, given its free and open-source nature.
+To address this, the [R Validation Hub](https://www.pharmar.org/) has
+released guidance documents focusing in this space.
+
+Given that the cost of the R package is non-chargeable, it can also
+serve as a potential tool for API integration. For instance, in signal
+detection, R packages can prove to be valuable tools due to the
+intricate derivation process for EBGM in the Bayesian approach, which
+aims to mitigate false positive signals resulting from multiple
+comparisons. The computation adjusts the observed-to-expected reporting
+ratio for temporal trends and confounding variables such as age and sex.
+While both methods can estimate this, the accessibility of R as free
+software enables easy integration into any system as an API or for macro
+estimation purposes without any copyrights issue. As always though
+consult the license of any package being used to be sure your usage is
+in compliance.
+
+## Identifying the Limitations in Using R Programming
+
+It is crucial to note that software cost is essential to any company,
+including Pharma and Biotechs. While R and RStudio® are free and SAS®
+requires an annual license, using R instead of SAS® may not always lower
+costs. The cost of software is only one part of the equation. To be used
+in a highly regulated industry such as pharmaceuticals, software
+validation, maintenance, and support are critical, and their costs need
+to be considered. Although R is free and open source, it comes with a
+learning curve, and in short term the industry might face a shortage of
+experienced pharma R programmers compared to those familiar with SAS®.
+
+## Leveraging the Right Tools to Capture Value
+
+Capturing the value of R programming starts with a clear vision for its
+use and a systematic approach to identifying and prioritizing the needs
+in the industry. Clinical Data Science is evolving rapidly, and the
+industry actively seeks alternative solutions to unlock valuable
+insights from diverse datasets. Recognizing the need for innovation,
+collaboration, and efficient tools is crucial. Rather than viewing SAS®,
+R, and Python as mutually exclusive, leveraging the strengths of each
+for appropriate Data Science problems provides a nuanced and effective
+approach.
+
+Ensuring data quality, scientific integrity, and regulatory compliance
+through risk assessment frameworks, validation, and documentation are
+imperative in this dynamic landscape. The pharmaceutical industry's
+journey toward embracing R reflects the broader trend of industries
+recognizing the value and potential of open-source tools in addressing
+complex challenges.
+
+![Leveraging the Right Tools](RightTool.png){fig-align="center"}
+
+## Author
+
+Venkatesan Balu is the Associate Director, Global Data Sciences, Navitas
+Life Sciences with 15+ years of experience in the Biostatistics domain,
+and in Phase I to Phase IV Clinical Trials across various therapeutic
+areas, BABE and PK studies. He has invaluable expertise in providing
+inputs to study design, sample size, SAP, outlier evaluation, interim
+analysis, complex statistical evaluation & model selection, and
+regulatory requirement. He is a technical leader in drug development
+strategy, adaptive design, portfolio optimization, and decision-making
+in clinical trials.
+
+<!--------------- appendices go here ----------------->
+
+```{r, echo=FALSE}
+source("appendix.R")
+insert_appendix(
+  repo_spec = "pharmaverse/blog",
+  name = long_slug
+)
+```