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Sensitivity analyses for the principal ignorability assumption using multiple imputation
Craig Wang1, Yufen Zhang2, Fabrizia Mealli3,4
1Department of Analytics, Novartis Pharma AG, Basel, Switzerland.
Pharmaceutical Statistics
|September 2, 2022
Summary
This study introduces a robust statistical method for analyzing clinical trial data, focusing on treatment effects in patient subpopulations affected by intercurrent events. The approach enhances reliability by assessing sensitivity to key assumptions, improving drug development insights.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Pharmacovigilance
Background:
- Intercurrent events in clinical trials complicate treatment effect interpretation.
- The ICH E9(R1) guideline proposes principal stratum strategy for subpopulation analysis.
- Principal ignorability assumption for estimation is unverifiable, necessitating sensitivity analyses.
Purpose of the Study:
- To develop and evaluate a robust statistical method for estimating treatment effects in principal strata.
- To assess the robustness of results to violations of the principal ignorability assumption.
- To apply the method to an oncology clinical trial context for time-to-event outcomes.
Main Methods:
- Proposed a joint model for multiple imputation of principal stratum membership and outcome.
- Incorporated varying degrees of principal ignorability violation into the model.
- Conducted simulation studies and applied the method to a synthetic oncology trial dataset.
Main Results:
- Joint imputation model-based approaches demonstrated superiority over naive subpopulation analyses.
- The sensitivity analysis successfully assessed treatment effects in the target subpopulation.
- The method provides a framework for evaluating robustness in clinical trial data.
Conclusions:
- The proposed joint modeling approach offers a more reliable method for analyzing treatment effects in subpopulations defined by intercurrent events.
- This strategy enhances the interpretability and robustness of clinical trial findings.
- The methodology has potential applications in various clinical settings and extensions for complex scenarios.
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