Multiply Robust Estimators in Longitudinal Studies with Missing Data Under Control-Based Imputation

• Activity Number: 235 - Recent Advancements in Nonparametric and Semiparametric Methodologies and Their Applications
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Siyi Liu* and Shu Yang and Yilong Zhang and Frank G. Liu
• Companies: North Carolina State University and North Carolina State University and Merck & Co., Inc. and Merck & Co., Inc
• Keywords: Longitudinal clinical trial; longitudinal observational study; semiparametric theory; sensitivity analysis; multiply robust; missing data

Abstract

Longitudinal studies are often subject to missing data. 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 classically envisioned control-based scenario, 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 obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves root n-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than the negative fourth root of n when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.