MacKenzie Todd A, Løberg Magnus, O'Malley A James
Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, U.S.A.
University of Oslo, Oslo, Norway.
Obs Stud. 2016 Mar;2:29-50. Epub 2016 Apr 24.
In randomized trials, the most commonly reported method of effect estimation is intention-to-treat (ITT), and to a lesser extent the per-protocol. The ITT is preferred because it is an unbiased estimator of the effect of treatment assignment. However, if there is any non-adherence the ITT is a biased estimate of the treatment effect, defined as the contrast between the potential outcome if treated versus the potential outcome if not treated. The treatment effect is most relevant to patients. Principal stratification is a framework for estimating treatment effects that combines potential outcomes and latent adherence strata. It yields an unbiased estimator of the complier average causal effect (CACE) for a difference in means or proportions, in the setting of all-or-nothing adherence. This paper addresses estimation of the causal hazard ratio for the compliers in a setting of right censoring of a time-to-event. We propose a novel approach to operationalizing principal stratification using weights. We report the results of simulations that vary the amount of adherence and selection bias that show the hazard ratio estimators we propose have minimal bias compared to the ITT, and per-protocol estimators. We demonstrate the approach using a population based randomized controlled trial of colorectal cancer screening subject to a high frequency of nonadherence in the screening arm.
在随机试验中，最常报告的效应估计方法是意向性分析（ITT），其次是符合方案分析。ITT 更受青睐，因为它是治疗分配效应的无偏估计量。然而，如果存在任何不依从情况，ITT 就是治疗效应的有偏估计，治疗效应定义为接受治疗时的潜在结果与未接受治疗时的潜在结果之间的对比。治疗效应与患者最为相关。主分层是一种估计治疗效应的框架，它将潜在结果和潜在依从分层结合起来。在全有或全无依从的情况下，它能得出均值或比例差异的依从者平均因果效应（CACE）的无偏估计量。本文探讨在事件发生时间存在右删失的情况下，依从者的因果风险比估计问题。我们提出一种使用权重来实施主分层的新方法。我们报告了模拟结果，这些模拟改变了依从程度和选择偏倚的量，结果表明与 ITT 和符合方案估计量相比，我们提出的风险比估计量偏差最小。我们使用一项基于人群的结直肠癌筛查随机对照试验来演示该方法，该试验中筛查组的不依从频率较高。
