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Robust Identification and Adjustment for Effect Modifiers in Indirect Treatment Comparisons; When and How?

When and How to Adjust for Effect Modifiers in Population-Adjusted Indirect Treatment Comparisons What Not to Do

Different methods of population-adjusted indirect treatment comparisons (PAICs) have been proposed to “correct” for the role of effect modifiers (EMs). Recently, case-studies demonstrated the circumstances when these assumptions can be reliably justified which mainly depend on data availability (sample size), type of outcomes and other statistical considerations.

The second webinar in this series will address the following questions:

  • How to reliably select EMs for inclusion in the PAICs?
  • What to do when published evidence on EMs contradicts with evidence from trial individual patient-data?
  • Should adjusting for EMs be the “norm” and when is best not to adjust for EMs?

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Michael Groff

Michael Groff is a Senior Research Consultant who has been active in HEOR for five years. His domains of specialty include health economics statistics and specifically population adjustment, for which he has contributed several original research pieces since joining Cytel. His work in population matching has contributed to the success of several HTA recommendations for oncology products in both Canada and the United Kingdom.

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