Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods
Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods
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DOI：
10.1191/0962280205sm403oa
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发表时间：
2005-08-01
影响因子：
2.3
通讯作者：
Tomenson, B
中科院分区：
文献类型：
--
作者：
Dunn, G;Maracy, M;Tomenson, B
完美实施的随机临床试验，特别是复杂干预措施的临床试验，极其罕见。它们的特点几乎总是不完全遵守随机分配的治疗方案，以及丢失不同数量的结果数据。在这里，我们首先描述各种各样的例子，然后介绍用于分析这类试验的工具变量方法。我们主要关注依从性要么全有要么什么都没有的情况(患者要么接受分配的治疗，要么没有--在后一种情况下，他们可能不接受治疗，或者接受分配的治疗以外的治疗)。这篇综述的主要目的是说明在关于缺失结果数据的机制的不同假设下，使用最大似然法的潜在类(有限混合)模型对编译器-平均因果效应进行估计。
Perfectly implemented randomized clinical trials, particularly of complex interventions, are extremely rare. Almost always they are characterized by imperfect adherence to the randomly allocated treatment and variable amounts of missing outcome data. Here we start by describing a wide variety of examples and then introduce instrumental variable methods for the analysis of such trials. We concentrate mainly on situations in which compliance is all or nothing ( either the patient receives the allocated treatment or they do not - in the latter case they may receive no treatment or a treatment other than the one allocated). The main purpose of the review is to illustrate the use of latent class ( finite mixture) models, using maximum likelihood, for complier-average causal effect estimation under varying assumptions concerning the mechanism of the missing outcome data.