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 (i) new IP-weights for estimating parameters of the MSM that depends on partial treatment history and (ii) closed testing procedures for selecting partial treatment history (how far back in time the MSM depends on past treatments). We derive the theoretical properties of our proposed methods under known IP-weights and discuss their extension to estimated IP-weights. Although some of our theoretical results are derived under additional assumptions beyond standard identifiability assumptions, some of which can be checked empirically from the data. In simulation studies, our proposed methods outperformed existing methods both in terms of performance in estimating time-varying treatment effects and in selecting partial treatment history. Our proposed methods have also been applied to real data of hemodialysis patients with reasonable results.

翻译：在正确的模型设定和可识别性假设下，即使存在时变混杂，边际结构模型（MSMs）的逆概率（IP）加权仍能提供时变治疗效果的一致估计量。然而，该方法存在两个问题：（i）由于IP权重累积所有时间点而导致的效率低下；（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：（i）用于估计依赖于部分治疗历史的MSM参数的新IP权重；（ii）用于选择部分治疗历史（即MSM依赖于过去治疗的时间回溯深度）的闭合检验程序。我们在已知IP权重下推导了所提方法的理论性质，并讨论了将其扩展到估计IP权重的情况。尽管我们的一些理论结果是在标准可识别性假设之外的附加假设下推导的，但其中部分假设可通过数据经验性检验。在模拟研究中，我们提出的方法在估计时变治疗效果和选择部分治疗历史方面的表现均优于现有方法。我们提出的方法也已应用于血液透析患者的真实数据，并获得了合理的结果。