Dunn Graham, Maracy Mohammad, Tomenson Barbara
Biostatistics Group, Division of Epidemiology and Health Sciences, University of Manchester, UK.
Stat Methods Med Res. 2005 Aug;14(4):369-95. doi: 10.1191/0962280205sm403oa.
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.
完美实施的随机临床试验，尤其是针对复杂干预措施的试验极为罕见。几乎所有这类试验的特点都是对随机分配的治疗方案的依从性欠佳，以及存在数量不等的缺失结局数据。在此，我们首先描述各种各样的例子，然后介绍用于此类试验分析的工具变量方法。我们主要关注依从性为全有或全无的情况（即患者要么接受分配的治疗，要么不接受——在后一种情况下，他们可能未接受任何治疗，或者接受的是分配之外的其他治疗）。本综述的主要目的是说明如何使用潜在类别（有限混合）模型，通过最大似然法，在关于缺失结局数据机制的不同假设下估计依从者平均因果效应。
