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Sensitivity Analysis in Longitudinal Clinical Trials via Distributional Imputation by Liu, Siyi; Yang, Shu; Zhang, Yilong; Guanghan; Liu is a scholarly article available to read on EtoBox.

What is Sensitivity Analysis in Longitudinal Clinical Trials via Distributional Imputation about?

Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analysis is critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies often request using imputation models such as return-to-baseline, control-based, and washout imputation. Multiple imputation is popular in sensitivity analysis; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin's combining rule. We propose distributional imputation (DI) in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the DI estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The finite-sample performance of DI inferen

Author

Liu, Siyi; Yang, Shu; Zhang, Yilong; Guanghan; Liu

Published

2022

Language

EN