Open Forum: Open Source Technologies in Clinical Data Analysis

The Open Source Technology in Clinical Data Analysis (OSTCDA) project was set up with the aim to create a manuscript on the integration of open-source software solutions for clinical data management, analysis and reporting.

A significant amount of time and energy has been invested in recent years exploring the desirability (do we want it?), feasibility (can we do it?), and viability (is it worth it?) of integrating open source solutions into our clinical data pipelines which transform source data into clinical study reports and submission data packages. In this October edition of the Open Source Open Forums, we will update on the status of this initiative and continue to hear from you on what we’ve missed so far.  

When this manuscript is complete, we hope to put to rest some of the burning questions that we believe we now know the answers to. This will allow industry, and all the passionate people in it, to look ahead and start tackling the next horizon of challenges related to using open source solutions for clinical data pipelines. We hope you will contribute your expertise to this effort.

Questions we will address during this Webinar:

• Which of the 17 questions posed has the industry answered – and how do we know?
  • What is Open Source?
  • What is the 'why' for using open-source solutions in Pharma clinical data analytics?

  • Can an open-source solution be trusted to be accurate? What are the relevant considerations?

  • What is the true cost of implementing open-source solutions into clinical data analytic processes?

  • How do you document your trust in an open-source solution to satisfy a third-party inquiry?

  • Will the FDA accept data and analyses generated with solutions developed and available as open source? 

  • Will other regulatory agencies accept data and analyses generated with solutions developed and available as open source?

  • How do you establish reproducibility and traceability with open source solutions - e.g., R Package management?

  • How do you support users in managing an ever-evolving environment of Open Source packages?

  • How do you transform the traditional Statistical Programmer/Analyst in Pharma who primarily codes in SAS - into the future Data Scientist with multiple tools, object oriented, code review, GitHub and git versioning, software development, agile, etc.?

  • Do we need to match SAS numerically when using a different language?

  • How do we ensure that the solutions being developed today, which we build dependencies on, will maintain long term viability, sustainability, maintainability?

  • Is it possible for industry fund open source?

  • Are contributors to open source exposing themselves to any liability of their solutions?

  • Are there any legal concerns or ramifications from open source development (on the user, developer, organisation)?

  • What open source models are available for businesses?

  • What else can we do?

• Which of the remaining questions are of most importance to address next?

In addition to the GitHub Discussion, we are hosting a series of forums throughout the year. See the Working Group Events page for further details.

This Forum will be taking place over Zoom on 11 October at 15:00-16:00 (BST) / 10:00-11:00 (EDT).

 REGISTER NOW