Abstract Number:
2573
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Myeongjong Kang (1), Morshed Alam (1)
Institutions:
(1) Merck & Co., North Wales, PA
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Incomplete longitudinal data are commonly encountered in clinical trials. To accurately handle intercurrent events and precisely characterize treatment effects, it is crucial to employ imputation methods that align with the estimand of interest. The magnitude of bias and variance in analysis outcomes depends not only on the chosen imputation methods but also on various factors, including the missing mechanism. Accordingly, conducting a comprehensive evaluation of the impact of imputation methods within a particular estimand framework is essential. This evaluation should involve numerical experiments across diverse simulation scenarios. For practical applicability, it is equally important to compare frequentist and Bayesian versions of imputation methods. In consideration of the treatment policy strategy, our study presents a simulation-based comparison of popular multiple imputation methods, including retrieved-dropout and control-based-mean imputations. This comparison encompasses different analysis models and scenarios of treatment discontinuation.
Keywords:
Estimand|Intercurrent events|Treatment policy strategy|ICH E9 (R1)|Multiple imputation|Missing not at random
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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