The Quantitative Decision-making Special Interest Group (QDM SIG) was formed in October 2017. It is a group of statisticians from industry and academia, with experience and interests in statistical methods for quantitative decision-making in drug development. The objectives of the SIG are:

  • To share (anonymized) case studies of how quantitative decision-making methods have been used within pharmaceutical companies, for decisions at the trial level, at the development level and at the portfolio level
  • To perform literature reviews, discuss and make recommendations on existing methodologies in terms of approach and interpretation
  • To develop new methodologies or practices where needed
  • To promote the role of the statistician in supporting decision-making in pharmaceutical companies and/or other stakeholders
  • To propose trainings, public meetings or publications to share methods and experience

For further information, or to join the QDM SIG, please contact the co-chairs:

Pierre Colin (Sanofi)
pierre.colin@sanofi.com

and

Gaelle Saint-Hilary (Politecnico di Torino)
gsainthilary@gmail.com

Latest News


Show all news

EventsFuture Events


  • Webinar: MCP-Mod – Theory, Implementation and Extensions - Dates: 08 – 08 May, 2019

    MCP-Mod (Multiple Comparisons & Modelling) is a popular statistical methodology for model-based design and analysis of dose finding studies. This webinar will describe the theory behind MCP-Mod (plus extensions), and how to implement it within available software. Pantelis Vlachos (Cytel) will provide a brief introduction to the methodology and illustrate the MCP-MoD capabilities in EAST 6.5. Saswati Saha (University of Brehem) will discuss new variations and alternatives to MCP-Mod and show how to implement them in R. Neal Thomas (Pfizer) will present further technical details of MCP-Mod by evaluating the method using results from least squares linear model theory.
  • PSI Toxicology SIG Workshop 2019 - Dates: 02 – 03 Apr, 2019

    This 1.5-day workshop will involve approximately 20 statisticians, focusing on discussions around “best practice” in the statistical analysis of various data types.​