Antonelli Joseph, Han Bing, Cefalu Matthew
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, U.S.A.
RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, U.S.A.
Stat Med. 2017 Dec 20;36(29):4604-4615. doi: 10.1002/sim.7447. Epub 2017 Aug 18.
A critical issue in the analysis of clinical trials is patients' noncompliance to assigned treatments. In the context of a binary treatment with all or nothing compliance, the intent-to-treat analysis is a straightforward approach to estimating the effectiveness of the trial. In contrast, there exist 3 commonly used estimators with varying statistical properties for the efficacy of the trial, formally known as the complier-average causal effect. The instrumental variable estimator may be unbiased but can be extremely variable in many settings. The as treated and per protocol estimators are usually more efficient than the instrumental variable estimator, but they may suffer from selection bias. We propose a synthetic approach that incorporates all 3 estimators in a data-driven manner. The synthetic estimator is a linear convex combination of the instrumental variable, per protocol, and as treated estimators, resembling the popular model-averaging approach in the statistical literature. However, our synthetic approach is nonparametric; thus, it is applicable to a variety of outcome types without specific distributional assumptions. We also discuss the construction of the synthetic estimator using an analytic form derived from a simple normal mixture distribution. We apply the synthetic approach to a clinical trial for post-traumatic stress disorder.
临床试验分析中的一个关键问题是患者对指定治疗的不依从性。在具有全有或全无依从性的二元治疗背景下，意向性分析是估计试验有效性的一种直接方法。相比之下，对于试验疗效存在3种常用的估计量，其具有不同的统计特性，正式名称为依从者平均因果效应。工具变量估计量可能无偏，但在许多情况下可能极具变异性。接受治疗和符合方案估计量通常比工具变量估计量更有效，但它们可能存在选择偏倚。我们提出一种综合方法，以数据驱动的方式纳入所有这3种估计量。综合估计量是工具变量、符合方案和接受治疗估计量的线性凸组合，类似于统计文献中流行的模型平均方法。然而，我们的综合方法是非参数的；因此，它适用于各种结果类型，无需特定的分布假设。我们还讨论了使用从简单正态混合分布导出的解析形式构建综合估计量。我们将综合方法应用于一项创伤后应激障碍的临床试验。
