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  • Working Groups
    • Data Transparency
    • Data Visualisation & Open Source Technology
      • Clinical Visual Analytics for Review and Submission (CVARS)
      • Communication of Version Metadata for Open-Source Languages
      • Comparing Analysis Method Implementations in Software (CAMIS)
      • Demonstrating Real-World Impact of Modernization of Statistical Analytics (MSA) Framework
      • Teal Enhancements for Cross-Industry Adoption
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    Comparing Analysis Method Implementations in Software (CAMIS)

      Comparing Analysis Method Implementations in Software (CAMIS)

      Oct 01, 2025

      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 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

      Lyn Taylor, Parexel

      lyn.taylor@parexel.com 

      Christina Fillmore, GSK

      christina.e.fillmore@gsk.com

      Yannick Vandendijck, Johnson & Johnson

      yvanden2@its.jnj.com

      Alex Pearce, PHUSE Project Assistant

      alexandra@phuse.global

      CURRENT STATUS (Updated Quarterly) 

      • Revamp of landing & help pages​
      • Corrections to Wilcoxon rank sum & ANOVA/ANCOVA & Survival​
      • New pages for Tipping point analysis​
      • New pages for Propensity score matching​
      • New pages for Recurrent events​
      • Attended PSI conference with2 x presentations​
      • Finalise Sample Size comparison pages​
      • Update CI for props to include cicalc package​
      • Team to present at PHUSE EU Connect​
      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

      Ongoing

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