Performance of LTMLE in the Presence of Missing Data in Control-Matched Longitudinal Studies

Abstract

Conventional controlled trials employ randomization and blinding to ensure the balance of baseline covariates between study arms. Treatment effect is formally tested via a pre-specified hypothesis reflecting trial’s primary objective defined by the primary efficacy endpoint, such as, is experimental treatment effective in slowing cognitive decline at a pre-specified landmark time in a neurologic therapeutic development? To address the same clinical question, but, as a safety endpoint in an observational study post radiopharmaceutical imaging drug approval due to concerns of radiation retention in the brain after accumulated real-world use over time, the targeted minimum loss-based estimation (TMLE) method has been suggested. Overall, for a longitudinal control-matched cohort study, assessing treatment effect (efficacy or safety) in the presence of missingness can be very challenging, which depends also on trial duration and missingness pattern of outcome data between study arms. TMLE is a two-step procedure to estimate a target parameter and has been shown to be doubly robust. The objectives of our research are a few folds. First, we investigate the performance of the TMLE method using longitudinal structure (LTMLE) in a prospective control-matched longitudinal cohort study. Second, we evaluate the performance of LTMLE and a few methods that are often proposed in regulatory submissions for longitudinal data analysis or in the presence of missing data with various missing data mechanisms. Third, we assess the impacts of various missing data mechanisms to treatment effect estimation via extensive simulation studies. Finally, we discuss the results of the simulation studies and their implications to conducting a feasible real-world control-matched longitudinal cohort study.