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Applying the Principal Stratum Strategy in Equivalence Trials: A Case Study
Jerome Sepin1,2, Thomas P A Debray3,4, Wei Wei1
1Biogen International GmbH, Baar, Switzerland.
Pharmaceutical Statistics
|May 6, 2025
Summary
The principal stratum strategy offers a robust method for estimating causal treatment effects in specific patient groups, particularly when dealing with intercurrent events like anti-drug antibodies in biosimilar trials. This approach addresses biases inherent in traditional subgroup analyses.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Pharmacovigilance
Background:
- The ICH E9 (R1) Addendum introduced the estimand framework to refine research questions in clinical trials.
- Intercurrent events (ICE) require specific strategies for accurate treatment effect estimation.
- Traditional subgroup analyses based on post-randomization variables like immunogenicity can introduce bias.
Purpose of the Study:
- To examine the principal stratum strategy for estimating causal treatment effects in specific subpopulations.
- To address the challenges of using post-randomization variables, such as anti-drug antibodies (ADAs), as intercurrent events.
- To demonstrate the implementation of the principal stratum strategy in a biosimilar trial.
Main Methods:
- Utilized the principal stratum strategy, leveraging counterfactuals and a missing data perspective.
- Applied a multiple imputation approach with longitudinal measurements.
- Implemented the strategy in a Phase 3 equivalence trial for a rheumatoid arthritis biosimilar.
Main Results:
- The principal stratum strategy enables estimation of treatment effects in subpopulations defined by intercurrent events.
- Demonstrated a method to create analysis datasets for subpopulations with ADAs as ICE.
- Highlighted the strategy's reliance on unobserved states and the need for complex modeling.
Conclusions:
- The principal stratum strategy provides a statistically sound pathway for estimating causal effects in specific subpopulations affected by intercurrent events.
- This method offers an alternative to biased subgroup analyses for variables like immunogenicity.
- Successful implementation requires rigorous modeling and careful consideration of assumptions regarding unobserved states.
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