Hospital for Special Surgery, Weill Cornell Medical College, New York, NY, USA.
Stat Med. 2011 Aug 30;30(19):2349-62. doi: 10.1002/sim.4296. Epub 2011 Jul 12.
In many clinical trials, compliance with assigned treatment could be measured on a continuous scale (e.g., the proportion of assigned treatment actually taken). In general, inference about principal causal effects can be challenging, particularly when there are two active treatments; the problem is exacerbated for continuous measures of compliance. We address this issue by first proposing a structural model for the principal effects. We then specify compliance models within each arm of the study. These marginal models are identifiable. The joint distribution of the observed and counterfactual compliance variables is assumed to follow a Gaussian copula model, which links the two marginal models and includes a dependence parameter that cannot be identified. This dependence parameter can be varied as part of a sensitivity analysis. We illustrate the methodology with an analysis of data from a smoking cessation trial. As part of the analysis, we estimate causal effects at particular levels of the compliance variables and within subpopulations that have similar compliance behavior.
在许多临床试验中，可以连续地衡量（例如，实际服用的指定治疗药物的比例）对指定治疗的依从性。一般来说，主要因果效应的推断具有挑战性，特别是当有两种积极的治疗方法时；对于依从性的连续测量，这个问题更加严重。我们通过首先为主要效应提出一个结构模型来解决这个问题。然后，我们在研究的每个臂中指定依从性模型。这些边际模型是可识别的。观察到的和反事实的依从性变量的联合分布被假设遵循高斯 Copula 模型，该模型连接了两个边际模型，并包含一个不能识别的依赖参数。该依赖参数可以作为敏感性分析的一部分进行调整。我们通过对戒烟试验数据的分析来说明该方法。在分析中，我们在依从性变量的特定水平和具有相似依从性行为的亚人群中估计因果效应。
