Posit at PharmaSUG
PharmaSUG, the annual conference dedicated to the application of technological solutions in data analytics and regulatory support, concluded on May 17th. It was a fantastic event, with Mike Stackhouse, Chief Innovation Officer and co-founder of Atorus Research, as the keynote speaker. The event provided a platform for professionals from the life science and health research sectors to share their experiences and insights. Looking through the schedule, we were delighted to see a significant presence of Posit tools among the attendees.
The conference showcased how Posit tools enhance research methodologies, streamline data analysis processes, and facilitate informed decision-making. The breadth of applications for Posit tools was truly impressive, and it was enlightening to see the diverse ways in which professionals integrated them into their workflows. Below are a few examples that caught our attention!
R Markdown is a literate programming tool that allows authors to create documents that combine text and code.
Blastula for Communicating Clinical Insights with R via Email
Our own Phil Bowsher showed how to easily produce and send HTML emails from R.
Meta-Analysis in R
Lin Gu from Duke University demonstrated how to produce publishing-quality forest plots for meta-analysis and use R Markdown to output them in different formats, including PDF, WORD, and PNG.
Using R to Create Population Pharmacokinetic Dataset
Yangwei Yan, Prema Sukumar, and Neelima Thanneer from Bristol Myers Squibb walked through generating datasets for Population Pharmacokinetics (PopPK) analysis in R Markdown (and with open-source R packages like the tidyverse’s lubridate).
Introduction to Shiny for Clinical Reporting
In this training seminar, Phil Bowsher provided a hands‐on introduction to Shiny with a focus on creating apps for inclusion in submissions to regulatory bodies.
RESTful Thinking: Using R Shiny and Python to streamline REST API requests and visualize REST API responses
Laura Elliott and Crystal Cheng from the SAS Institute Inc. shared how to create embedded HTTP requests in Shiny apps using Python.
Automation of the SDSP CBER Appendix for Vaccine Studies
Nicole Jones and Pritesh Solanki from Merck presented a Shiny application developed to semi-automate the generation of Study Data Standardization Plan Center of Biologics Evaluation and Research (SDSP CBER) tables.
Automated Mockup Table and Metadata Generator
Jeff Cheng, Shunbing Zhao, Guowei Wu, and Suhas Sanjee from Merck described how Shiny streamlines tables, listings, and figures (TLFs) generation.
R Package Qualification: Automation and Documentation in a Regulated Environment
Paul Bernecki, Nicole Jones, Uday Preetham Palukuru, and Abhilash Chimbirithy from Merck described their work developing Shiny apps that present real-time results of qualification and automation of user qualification requests.
F2: FAIR and Filing – Assessing Data Fitness for FAIR and Filing
Bidhya Basnet and Dyuthi Yellamraju from Roche-Genentech discussed the F2 dashboard, a Shiny app that displays their progress towards being FAIR (findable, accessible, interoperable, and reusable).
Generating Clinical Graphs in SAS and R – A Comparison of the Two Languages
Kriss Harris from SAS Specialists Ltd. and Endri Elnadav from Bayer AG showed how Shiny can be used to produce interactive plots and tables.
Posit spends a significant proportion of its engineering resources on open-source software and leads contributions to over 250 open-source projects.
R Tables via GT for Regulatory Submissions
Phil Bowsher demonstrated the gt package, a flexible and powerful R package for generating tables as part of your research and reporting TFL programming.
A Light-Weight Framework to Manage Programs and Run All the TLFs in R
Chi-Hua Huang from Astellas Pharma Global Development, Inc. presented a light-weight framework that combines base R, tidyverse, sassy, and rstudioapi packages in conjunction with MS Excel to efficiently produce/manage the TLFs for a deliverable.
An Introduction to Obtaining Test Statistics and P-Values from SAS® and R for Clinical Reporting
Brian Varney from Experis showed how to extract values from Chi-Square and Linear Models tests in a variety of packages, including broom, a tidymodels package.
The RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R.
Acceleration and automation of genomic data analysis to meet corporate compliance standards using advanced cloud components
Gopal Joshi, Satyoki Chatterjee, Pankaj Choudhary, Sanjay Koshatwar, and Shekhar Seera from Circulants shared their pipeline for analyzing gene expression at the transcriptional level. Their secondary data analysis is performed using Posit Workbench, the place for teams to collaboratively build open-source data science projects at scale.
Using R to Automate Clinical Trial Data Quality Review
Melanie Hullings, Emily Murphy, Derek Lawrence, Michelle Cohen, and Andrew Burd from TrialSpark described their use of RStudio to surface potential protocol deviations in an EDC dataset.
Learn more about how Posit products are used in pharma
The prevalence of Posit tools in the PharmaSUG schedule highlights their significant role in supporting research and analysis in the life sciences and health sectors. If you want to learn more about how Posit shows up in pharma, book a call with our experts today.
Join the pharma community driving innovation with open source
If you want to join the vanguard in the pharmaceutical field, register for posit::conf(2023)! We are excited to partner with R/Pharma to host their in-person program at our upcoming conference happening September 17-20th in Chicago. Leaders from various pharmaceutical companies such as Roche and Novartis will oversee the discussions focused on the “next-generation” open-source tooling for drug development.
We are hosting two activities specific to the pharmaceutical industry:
- The “R/Pharma Roundtable Summit” for program leaders and people leading Open Source initiatives, and
- “Leveraging And Contributing To The Pharmaverse For Clinical Trial Reporting In R” workshop for data professionals.