Reference-Based Multiple Imputation for Longitudinal Binary Data.

ABSTRACT:

Introduction

In clinical trials, a treatment policy strategy is often used to handle treatment nonadherence. However, estimation in this context is complicated when data are missing after treatment deviation. Reference-based multiple imputation has been developed for the analysis of a longitudinal continuous outcome in this setting. It has been shown that Rubin's variance estimator ensures that the proportional loss of information due to missing data is approximately the same as that seen in analysis under the missing-at-random assumption for a broad range of commonly used reference-based alternatives; that is it is information anchored. However, the best way to implement reference-based multiple imputation for longitudinal binary data is unclear.

Methods

We formulate and de

SUBMITTER: Cro S 

PROVIDER: S-EPMC11758479 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature