Taguri Masataka, Chiba Yasutaka
Yokohama City University, Japan.
Int J Biostat. 2012 Aug 7;8(1):24. doi: 10.1515/1557-4679.1386.
Abstract Noncompliance with assigned treatment is an important problem of randomized clinical trials. In this situation, the structural mean model (SMM) approach focuses on the average treatment effect among patients actually treated (ATT). In contrast, the principal stratification (PS) approach addresses the effect on a certain subgroup defined by latent compliance behavior. While these approaches target different causal effects, the estimators have the same form as the classical instrumental variable estimator, under the assumption of no effect modification (NEM) and monotonic selection. In this article, we clarify the relation between SMM and PS under the monotonic selection assumption. Specifically, we translate the NEM assumption for the SMM estimator into the words of the PS approach. Then, we propose a new bound for the ATT by making a possibly more plausible assumption than the NEM assumption based on the PS approach. Furthermore, we extend these results to the average treatment effect for the entire population. The proposed bounds are illustrated with applications to a real clinical trial data. Although our assumption cannot be empirically verified, the proposed bounds can be considerably tighter than those previously proposed.
摘要 不遵守指定治疗方案是随机临床试验中的一个重要问题。在这种情况下，结构均值模型（SMM）方法关注的是实际接受治疗的患者中的平均治疗效果（ATT）。相比之下，主分层（PS）方法关注的是由潜在依从行为定义的某个亚组上的治疗效果。虽然这些方法针对不同的因果效应，但在无效应修正（NEM）和单调选择假设下，估计量与经典工具变量估计量具有相同的形式。在本文中，我们阐明了在单调选择假设下SMM和PS之间的关系。具体而言，我们将SMM估计量的NEM假设用PS方法的语言进行表述。然后，基于PS方法，通过做出一个可能比NEM假设更合理的假设，我们提出了ATT的一个新的界。此外，我们将这些结果扩展到了整个人群的平均治疗效果。通过应用于一个真实的临床试验数据对所提出的界进行了说明。虽然我们的假设无法通过经验进行验证，但所提出的界可能比先前提出的界更紧。
