Inverse probability (IP) weighting of marginal structural models (MSMs) can provide consistent estimators of time-varying treatment effects under correct model specifications and identifiability assumptions, even in the presence of time-varying confounding. However, this method has two problems: (i) inefficiency due to IP-weights cumulating all time points and (ii) bias and inefficiency due to the MSM misspecification. To address these problems, we propose new IP-weights for estimating the parameters of the MSM dependent on partial treatment history and closed testing procedures for selecting the MSM under known IP-weights. In simulation studies, our proposed methods outperformed existing methods in terms of both performance in estimating time-varying treatment effects and in selecting the correct MSM. Our proposed methods were also applied to real data of hemodialysis patients with reasonable results.

翻译：在正确模型设定和可识别性假设下，即使存在时变混杂因素，边际结构模型（MSMs）的逆概率（IP）加权仍能提供时变治疗效应的一致估计量。然而，该方法存在两个问题：（i）由于IP权重累积了所有时间点而导致的效率低下，以及（ii）由于MSM设定错误而导致的偏倚和效率低下。为解决这些问题，我们提出了新的IP权重，用于估计依赖于部分治疗史的MSM参数，以及在已知IP权重下选择MSM的封闭检验程序。在模拟研究中，我们提出的方法在估计时变治疗效应的性能以及选择正确MSM方面均优于现有方法。我们提出的方法也应用于血液透析患者的真实数据，并获得了合理的结果。