1. Exploring Use of R for Clinical Trials, Kalpesh Prajapati
2. Is R language reliable and efficient tool for programming SAS datasets or just art for art’s sake?, Piotr Podlewsk [DPLYR, SDTMs]
3. Clinical Trial Datasets (CDISC - SDTM/ADaM) Using R, Prasanna Murugesan [Compare SAS Code, Presentation]
4. A Gentle Introduction to R From A SAS Programmer's Perspective, Saranya Duraisamy [Beginner Level]
5. Python and R made easy for the SAS® Programmer, Janet Li [Compare Code]
6. R for SAS programmers: It’s different, but friendly, Friedrich Schuster
7. SAS® and R - stop choosing, start combining and get benefits!, Diana Bulaienko
8. SAS and R Playing Nice Together, David Edwards, Bella Feng, Brian Schultheiss
9. Can clinical trial data sets (CDSIC - SDTM/ADaM) be generated using R? Blog
10. R: Validation Hub - A RISK-BASED APPROACH FOR ASSESSING R PACKAGE ACCURACY WITHIN A VALIDATED INFRASTRUCTURE [FDA, IQ/OQ/PQ]
11. CRAN Task View: Clinical Trial Design, Monitoring, and Analysis (packages)
12. SAS® and R Working Together, Matthew Cohen [Date formats]
13. Best Practices for Reproducible Package Management in R
14. Techniques for writing robust R programs, Martin Gregory , Merck Serono [Presentation, Defensive Programming]
15. How do I select an R package for my clinical workflow?, Sean Lopp & Phil Bowsher [Validation, Documentation, Version Control, Common FAQs]
16. The Challenges of Validating R [Presentation]
17. Using R to Drive Agility in Clinical Reporting [GSK Presentation]
18. CDISC Dataset-XML – A new Dataset Structure for Clinical Trial Data Transport for Future Drug Submissions, Jörg Dillert [R4CDISC, R4DSXML]
19. End to End Interactive TLF using R Shiny, Rohit Banga
20. Is there any better option than SAS for TLFs? Yes, there R!, Niccolo Bassani [Presentation - ddply(), Proc REPORT]
21. A quick introduction to plyr, Sean Anderson [PLYR package splits data]
22. Using R Programming for Clinical Trial Data Analysis Blog
23. The SAS® Versus R Debate in Industry and Academia, Chelsea Loomis Lofland, Rebecca Ottesen [Compare SAS and R]
24. Using R in a Regulatory Environment: some FDA perspectives
25. Using R: Perspectives of a FDA Statistial Reviewer [Presentation]
26. R-Pharma Papers
27. R for Clinical Reporting, Yes - Let's Explore It!, Hao Meng, Yating Gu, Yeshashwini Chenna (SASxport, sas7bdat, dplyr, White Paper)
28. Generating ADaM Compliant ADSL Dataset Using R, Vipin Kumpawat [SDTM, White Paper, Left Variable Assignment]
29. Generating TFLs in R - Challenges and Successes compared to SAS, Amol Waykar, Kevin Kramer, Kalyani Komarasetti, Andrew Miskell
30. Using the R interface in SAS ® to Call R Functions and Transfer Data, Bruce Gilsen
31. Expand Your Skills from SAS® to R with No Complications, Andrii Korchak [DPLYR, %>%]
32. Simulation in SAS with Comparisons to R, Chelsea Loomis Lofland, Rebecca Ottesen
33. Normal is Boring, Let’s be Shiny: Managing Projects in Statistical Programming Using the RStudio® Shiny® App, Girish Kankipati, Hao Meng
34. Building Automations for Generating R and SAS Code Supporting Visualizations Across Multiple Therapeutic Areas, Anastasia Alexeeva, William Martersteck, and Mei Zhao
35. Effective Exposure-Response Data Visualization and Report by Combining the Power of R and SAS Programming, Shuozhi Zuo, Hong Yan
36. A Brief Introduction to Performing Statistical Analysis in SAS, R & Python, Erica Goodrich, Daniel Sturgeon
37. The Power of Data Visualization in R, Babych Oleksandr
38. Open-NCA – R Scripts for CDISC-based Pharmacokinetic Analysis, Peter Schaefer
39. Statistical Computing Environments in CDER, Paul Schuette
40. Use of R Script to Create Trial Summary (ts.xpt) Domains for Nonclinical SEND Studies, Bob Friedman, Xybion; Anthony Fata, William Varady, William Houser, Kevin Snyder
41. R Package Oriented Software Development Life Cycle in Regulated Clinical Trial Environments, Yalin Zhu, Rinki Jajoo, Clare Bai, Sarad Nepal, Daniel Woodie, Keaven Anderson, Yilong Zhang [Presentation, GxP, SDLC]
42. R for SDTM and ADaM Data [Poster]
43. R syntax for SAS programmers, Max Cherny [Beginner, Tidyverse, SDTMs, White Paper]
44. Creating Graphs Simply with SAS® or R, John O’Leary, Jaclyn Scholl
45. Techniques for writing robust R programs, Martin Gregory , Merck Serono
46. Seamless R And SAS: For Shiny Visualizations, Pragathi Kotha Venkata [Presentation]
47. R for the Analysis of Clinical Data, Greg Jones [Presentation]
48. Numerical validation as a critical aspect in bringing R to the Clinical Research, Adrian Olszewski [R and SAS differences]
49. How much is it? Validation of Open-Source-Software Using the example of R, Peter Bewerunge [IQ/OQ/PQ]
50. Introducing LearnR package
51. An Automation Proof of Concept of Periodic Reporting via R Shiny [PSUR/DSUR, Presentation]
52. Open-Source Development for Traditional Clinical Reporting, Mike Stackhouse, Nathan Kosiba
53. r2rtf – an R Package to Produce Rich Text Format (RTF) Tables and Figures, Siruo Wang, Simiao Ye, Keaven Anderson, Yilong Zhang
54. Validating R – Part of the Uphill Battle in the Pharmaceutical Industry, Peter Schaefer and Debra Fontana [Installation, Operational, Performance Qualifications]
55. A Beginner’s Babblefish: Basic Skills for Translation Between R and SAS, Sarah Woodruff
56. Using R to Validate Results of Other Programming Environments, Nikhil Abhyankar, Vidyagouri Prayag
57. Drug Safety Reporting- now and then, David Garbutt
58. Exploring R as a validation tool for statistical programming in clinical trials, Uday Preetham Palukuru, Runcheng Li, Nileshkumar Patel, Changhong Shi [Extract rtf files]
59. Implementing and Achieving Organizational R Training Objectives, Jeff Cheng, Abhilash Chimbirithy, Amy Gillespie, and Yilong Zhang
60. Using R from end to end: CDISC SDTM and ADaM into .xpt and output generation, Antonio Rodriguez [Presentation]
61. Peaceful Co-existence: R and SAS, Martin Gregory, Manuel Cornes
62. ValidR Enterprise: Developing an R Validation Framework, Andy Nicholls
63. Defining Script Metadata for Sharing: Using phuse R package as an example, Hanming Tu [Presentation, HOW] [ValTools]
64. Making data mapping process easier and smarter with SAS and R, Andrii Artemchuk [Presentation, SAS Macros]
65. Sample size: a couple more hints to handle it right using SAS and R, Andrii Artemchuk [Presentation]
66. Pan-pharma Industry collaboration - new horizon, success stories [Presentation]
67. Closing the Gap, Creating and End-to-End R package toolkit for the clinical reporting pipeline, Eli Miller, Ben Straub [Presentation]
68. Introduction to R for Statistical Programmers [Presentation]
69. Creating and End-to-End R Package Toolkit for the Clinical Reporting Pipeline, Eli Miller, Ben Straub [Presentation]
70. The Practical R [book]
71. R Programming Data frame: Exercises, Practice, Solution
72. Programming R
73. Create a Demographics Table with gt Package in R
74. Open-Source Development for Traditional Clinical Reporting, Mike Stackhouse, Nathan Kosiba
75. R Validation: Approaches and Considerations, Phil Bowsher, Sean Lopp
76. Transitioning to R for Clinical Submissions [Presentation, Step by Step Instructions]
- Add RStudio as a Trusted Vendor
- Get users off the desktop
- Get a Server (cloud or onsite) for Development
- Create an R version and Package plan
- Created a Validated repository of packages based
on Plan in Step4
- As time goes on, Don't upgrade R
- Validate your internal packages & UDFs
- Determine your method for data management &
TLFs
- Harden Production and not RStudio
- Create a R cloud strategy for clinical reporting
77. Harmonizing your Traditional Workflow with R, Nick Masel, Mike Stackhouse [Presentation]
78. Patient Narrative Generation, PROC Report/Stream vs R - Markdown, Yuanyuan Gu and Mengmeng Zhao
79. Using R-Shiny Capabilities in Clinical Programming to create Applications, Prakasam R, Sujatha Kandukuri, Tyagrajan Swaminathan
80. Make your Data shine with R-Shiny, Pavan Vemuri [Patient Profiles, R code]
81. Using R Markdown to Generate Clinical Trials Summary Report [Presentation]
82. Using R Shiny to Explore Clinical Trial Data, Yaoxian Yuan, Jiaying Wu
83. A Process to Validate Internal Developed R Package under Regulatory Environment, Madhusudhan Ginnaram, Simiao Ye, Yalin Zhu, Yilong Zhang, Amin Shiraz
84. The reporter package: A Powerful and Easy-to-use Reporting Package for R, David Bosak
85. Open-Source Development for Traditional Clinical Reporting, Mike Stackhouse, Nathan Kosiba
86. Table-Based Data Visualization using R, Matthew Kumar [Presentation]
87. R Package Development at Novo Nordisk [Presentation, Submission]
88. Data Handling with SASMarkdown in R Studio, Nandukrishnan, Senju Mani
89. Shiny for Submissions, Phil Bowsher, Eric Nantz
90. A Beginner’s Roadmap to Map Functions for Clinical Reporting in R, Samuel Curlee, Gabriela Piasecki, Xiaoqing Tang, Wenjian Xu
91. FY2018 Regulatory Science Report: Data Analytics [Shiny]
92. Implementing A Risk-based Approach to R Validation, Andy Nicholls [Presentation, Riskmetric]
93. The Data Validation Cookbook, Mark P.J [Book]
94. Using the R interface in SAS ® to Call R Functions and Transfer Data, Bruce Gilsen
95. An Introduction to Obtaining Test Statistics and P-Values from SAS® and R for Clinical Reporting, Brian Varney
96. R Tables via GT for Regulatory Submissions, Phil Bowsher, Rich Iannone
97. Building an Internal R Package for Statistical Analysis and Reporting in Clinical Trials: A SAS User’s Perspective, Huei-Ling Chen, Heng Zhou, Nan Xiao
98. R Package Quality & Validation: Current Landscape, Phil Bowsher
99. admiralonco - the cross-company R package for Oncology admirers, Neharika Sharma, Matthew Marino
100. R Package Qualification: Automation and Documentation in a Regulated Environment, Paul Bernecki, Nicole Jones, Uday Preetham Palukuru, Abhilash Vasu Chimbirithy
101. Using R to Automate Clinical Trial Data Quality Review, Melanie Hullings, Emily Murphy, Andrew Burd, Derek Lawrence, Michelle Cohen
102. Real Projects, Real Transition, Really Revolutionary – Transitioning to R for Biometrics Work, Danielle Stephenson, Rebekah Oster and Alyssa Wittle
103. Are you planning to create/validate CDISC data set in R? Here is a step-bystep guide!, Ganesh chandra Gupta [SDTMs, ADaMs, White Paper]
