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Principal stratum strategy: Potential role in drug development.

Björn Bornkamp1, Kaspar Rufibach2, Jianchang Lin3

  • 1Clinical Development and Analytics, Novartis, Basel, Switzerland.

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|February 24, 2021
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Summary

Randomized trials can estimate causal effects, but post-randomization events complicate interpretation. The principal stratum strategy and ICH E9(R1) estimand framework offer improved methods for analyzing treatment effects in drug development.

Keywords:
causal inferenceestimandintercurrent eventpotential outcomesrandomization

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Randomized trials estimate causal treatment effects in overall and sub-populations.
  • Post-randomization events, termed intercurrent events, can complicate causal effect estimation.
  • Standard analyses may fail to provide causal estimates when intercurrent events are treatment-related.

Purpose of the Study:

  • To address challenges in estimating causal treatment effects when intercurrent events occur post-randomization.
  • To introduce the principal stratum strategy as an alternative analytical approach.
  • To advocate for the ICH E9(R1) estimand framework for handling intercurrent events.

Main Methods:

  • Utilizing the principal stratum strategy to classify subjects based on potential intercurrent event occurrence.
  • Applying the ICH E9(R1) estimand framework to formulate and address clinical questions.
  • Illustrating the approach with examples from drug development.

Main Results:

  • Principal strata questions are common in drug development.
  • The ICH E9(R1) estimand framework enhances transparency and adequacy of analyses.
  • Key assumptions for principal strata estimation are identified, many of which are unverifiable.

Conclusions:

  • The principal stratum strategy and ICH E9(R1) framework provide a more robust approach to intercurrent events.
  • Transparent assumptions and adequate analyses are crucial for reliable conclusions.
  • Sensitivity analyses are essential to evaluate the robustness of findings based on unverifiable assumptions.