Estimate Time-Varying Exposure Effects via Ensemble Learning-based Marginal Structural Model with Application to Adolescent Cognitive Development Study

Abstract

Evaluating the effects of time-varying exposures is essential for longitudinal studies. The effect estimation becomes increasingly challenging when dealing with hundreds of time-dependent confounders. We propose a Marginal Structure Ensemble Learning Model (MASE) to provide a marginal structure model (MSM)-based robust estimator under the longitudinal setting. The proposed model integrates multiple machine learning algorithms to model propensity scores and a sequence of conditional outcome means such that it becomes less sensitive to model mis-specification due to any single algorithm and allows many confounders with potential non-linear confounding effects to reduce the risk of inconsistent estimation. Extensive simulation analysis demonstrates the superiority of MASE over benchmark methods (e.g., MSM, G-computation, Targeted maximum likelihood), yielding smaller estimation bias and improved inference accuracy. We apply MASE to the adolescent cognitive development study to investigate the time-varying effects of sleep insufficiency on cognitive performance. The results reveal an aggregated negative impact of insufficient sleep on cognitive development among youth.