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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

CAMIS 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



Project Leads

Email

Michael Rimler

michael.s.rimler@gsk.com

Mike Stackhouse

mike.stackhouse@atorusresearch.com 

Lauren White (PHUSE Project Coordinator)lauren@phuse.global

Lyn Taylor, Parexel

lyn.taylor@parexel.com 

Christina Fillmore, GSK

christina.e.fillmore@gsk.com

Harshal Khanolkar, Novo Nordisk

hlsk@novonordisk.com

Kerry Robson, PHUSE Project Assistant

kerry@phuse.global


Status
colourBlue
titleCurrent Status
Q1 2024

Launched CAMIS-ONCO. Expanded membership and industry awareness of the project. Increased content loaded into the repository CAMIS (psiaims.github.io)



Objectives & DeliverablesTimelines

Key Milestones 

March 2021
ObjectiveTimeline

Recruit team members

Nov 2020

Initial collated (extensible) list of common statistical analyses across the industry

Dec 2020

GitHub Repository documenting identified differences between statistical analysis implementations (based on R and SAS use cases as a starting point)

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

June 2021Q2 2023

Expand GitHub repository to incorporate Python and/or Julia

Sep 2021Q3 2023

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

Dec 2021

Project Members 

Organisation 
Aiming Yang Merck

Andy Miskell

Eli Lilly

Andy Nicholls

GSK
Chung-kai Sun  Janssen Research and Development 
Clara Beck  Chrestos

Doug Thompson

GSK

Joseph Rickert

RStudio

Kyle Lee

FDA
Ke Wang Novartis
Matthew Kumar  Bayer
Mia QI  Janssen Research and Development 
Min-Hua Jen Eli Lilly
Steve WalkerExperis

Status
colourBlue
titleCurrent Status
 Q42020

Initialized group, began exploration of partnerships on project with PSI AIMS, the R Validation Hub, and the R Consortium. Began initial recruiting of core team. 

Ongoing