Principal Component Causal Estimands for Equivalence Trials in the Presence of Intercurrent Events

• Activity Number: 55 - Recent Evaluations of Methods for Handling Noncompliance/Dropouts in Clinical Trials for Better Guidance Driven Application
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Wanjie Sun*
• Companies: FDA
• Keywords: Causal Inference; Estimand; Equivalence; Sensitivity; ITT; PP

Abstract

In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is based on the observed per-protocol (PP) population (usually completers and compliers). However, missing data and non-compliance are post-randomization intercurrent events and may introduce selection bias. Therefore, PP analysis is generally not causal. The FDA Missing Data Working Group recommended using “causal estimands of primary interest.” In this paper, we propose a principal stratification causal framework and co-primary causal estimands to test equivalence, which was also recommended by the recently published ICH E9 (R1) addendum to address intercurrent events. We identify three conditions under which the current PP estimator is unbiased for the proposed primary causal estimands – the “Survivor Average Causal Effect” (SACE) estimand. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator when sensitivity parameters vary within a clinically meaningful range. The proposed method also applies to comparative clinical studies for biosimilar products.