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What features are most beneficial to stat programming? What are the benefits of Git?

There is interest across the pharmaceutical industry in using Git for statistical programming. Some companies have already incorporated Git, or are attempting to, and there are common challenges being faced. In these cases, the introduction of Git has increased the complexity of a statistical programmer’s tasks, which has led to challenges in uptake and to taking advantage of the benefits Git offers. This is an important challenge to overcome for increasing efficiency of operations. The purpose of this Working Group project is to provide the industry with tools and guidance for addressing and overcoming this common challenge.

Project Scope

Problem Statement  

The clinical study analysis and reporting QC process has not changed in decades. It is widely agreed that there is significant overhead in using double programming, and that this may need to be revisited to accelerate and optimally use programming resources. This process has not been revisited or analysed within a modern context. The purpose of this Working Group project would be to perform this analysis and report findings.

The QC process has significant impact on the adoption of Git in statistical programming, specifically in the complexity in using Git in a way that does not disrupt the QC process. This will also impact on what is perceived as acceptable level of QC for submissions by the industry, including regulators.


Project Scope

The scope will include the following topics:

  • State of the union: how QC is broadly performed across the industry
  • What obstacles does double programming introduce when it comes to accelerating and making use of modern technology and techniques?
  • Alternatives to the current process
  • What impact does the adoption of Agile methodology have on the current QC process?
  • What are other regulated industries, such as banking and insurance, doing?
  • What is the cost of QC, and does changing the process impact on the cost positively or negatively?

How might the increasing use of AI change the QC process?



Project LeadsEmail

Caroline Phares, Altair

cphares@altair.com

Nicola Newton
Matthew Finnemeyer, Vertex Pharmaceuticals IncMatthew_Finnemeyer@vrtx.com
Korak Datta, AstraZenecakorak.datta@astrazeneca.com
Alex Pearce, PHUSE Project
Assistant
Assistant 
nicky@phuse

alexandra@phuse.global


Project Accepted

Status
colourBlue
titleCurrent Status
Q1 2025 

Q2 2025 

    • Charter finalised and locked.​
    • PHUSE US Connect 2026 abstract drafted.​
    • Research fully underway.​




Objectives & Deliverables

Timelines

1. Principles for using Git and example workflow(s) in context of the current QC process

2. PHUSE white paper on Git for statistical programming, to cover:

  • The case for Git
  • Challenges in using Git
  • Current QC process
  • Using Git for audit trails
  • How Git can work with the current QC process
  • How Git is typically used outside of statistical programming and why
  • What the QC process could look like when using the benefits of a typical Git framework
  • Regulators’ point of view

3. Core education on Git for statistical programmers

4. Soft proposal for an alternative QC process using Git featuresWhite Paper


TBC