Multiply robust estimators in longitudinal studies with missing data under control-based imputation.

ABSTRACT: Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obt

SUBMITTER: Liu S 

PROVIDER: S-EPMC10885818 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature