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Project Scope

Comparing Analysis Method Implementations in Software (CAMIS) has evolved from the Clinical Statistical Reporting in a Multilingual World project.

Several discrepancies have been discovered in statistical analysis results between different programming languages, even in fully qualified statistical computing environments. Subtle differences exist between the fundamental approaches implemented by each language, yielding differences in results which are each correct in their own right. The fact that these differences exist causes unease on the behalf of sponsor companies when submitting to a regulatory agency, as it is uncertain if the agency will view these differences as problematic. Understanding the agency’s expectations will contribute significantly to enabling the broader adoption of multiple programming languages in the production of data submission packages for regulatory review

The Clinical Statistical Reporting in a Multilingual World project seeks to clearly define this problem and provide a framework for assessing the fundamental differences for a particular statistical analysis across languages. In this context, the risk of interpreting numerical differences in analysis results due solely to differences in programming language can be mitigated, instilling confidence in both the sponsor company and the agency during the review period. This will be accomplished by:

  1. Identifying common statistical analyses performed during submissions to narrow the scope of where discrepancies must be identified (e.g., continuous summaries, frequency counts, hazard models, bioequivalence testing, steady-state assessments, bioavailability testing, ANOVA)

  2. Providing necessary documentation to produce equivalence in results between separate statistical analysis software packages/languages (where possible)

  3. Evaluating and documenting differences in results between popular statistical analysis implementations as use cases

  4. Provision of sample code for use cases through a publicly accessible code repository for both review and consumption

  5. Promoting the notion that the ‘right’ implementation of a particular statistical analysis should be based sound statistical reasoning and not limited by the capabilities of a specific programming language or statistical analysis software package, nor its default settings

The CAMIS repository to document known differences is now live and open for community contributions. The draft White Paper can be found here. 


Project Leads

Email

Lyn Taylor (Parexel) 

lyn.taylor@parexel.com

Christina Fillmore (GSK)

Christina.Fillmore@gsk.com

Paula Rowley (PHUSE Project Assistant)paula@phuse.global



Objectives & DeliverablesTimelines

Expand repository to provide comparable syntax across languages (based on R and SAS use cases as a starting point)

Q2 2023

Expand GitHub repository to incorporate Python and/or Julia

Q3 2023

White Paper providing framework for addressing language discrepancies in statistical analysis implementations, including specific use cases as examples

Q4 2023


Status
colourBlue
titleCurrent Status
Q1 2023

Project are working on white paper.



Project Members 

Organisation 
Aiming Yang Merck, Sharp & Dohme
Amrit Singh Bayer

Andy Miskell

Eli Lilly

Andy Nicholls

GSK
Brian Varney Experis
Chung-kai Sun  Janssen Research & Development 
Clara Beck  Chrestos

Doug Thompson

GSK
Harshal KhanolkarNovo Nordisk

Joseph Rickert

RStudio
Karnika DalalRegeneron
Kevin PutschkoExperis

Kyle Lee

FDA
Ke Wang Novartis
Michael Kane Yale University 
Matthew Kumar  Bayer
Mia QI  Janssen Research & Development 
Min-Hua Jen Eli Lilly
Soren Lophaven Omicron

Suhas Ravindra

GSK
Steve WalkerExperis