标题： 基于集成学习的边缘结构模型估计时变暴露效应及其在青少年认知发展研究中的应用 显示英文标题

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

摘要： 评估随时间变化的暴露效应对于纵向研究至关重要。 当处理数百个时间依赖性混杂因素时，效应估计变得越来越具有挑战性。 我们提出了一种边缘结构集成学习模型（MASE），以在纵向设置下提供基于边缘结构模型（MSM）的稳健估计量。 所提出的模型整合了多种机器学习算法来建模倾向得分和一系列条件结果均值，从而使它对由于任何单一算法导致的模型误设不那么敏感，并允许许多具有潜在非线性混杂效应的混杂因素，以降低不一致估计的风险。 广泛的模拟分析证明了MASE优于基准方法（例如，MSM、G-computation、目标最大似然），产生了更小的估计偏差和改进的推断准确性。 我们将MASE应用于青少年认知发展研究，以调查睡眠不足对认知表现的随时间变化的影响。 结果揭示了睡眠不足对青年认知发展的总体负面影响。 显示英文摘要

摘要： 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.