因果生存分析:评估不依从性随机临床试验治疗意向和方案效果的指南

E. Murray, E. Caniglia, L. Petito
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引用次数: 22

摘要

当报告随机实验的结果时,研究人员通常选择在意向治疗效应之外呈现每个方案效应。然而,这些协议效应通常是回顾性描述的,例如,在整个研究过程中比较坚持指定治疗策略的个体的结果。这种对每个方案效应的回顾性定义经常被混淆,不能被因果解释,因为它遇到了治疗-混杂因素反馈循环,其中过去的混杂因素影响未来的治疗,当前的治疗影响未来的混杂因素。使用这种方法估计的每方案效应高度容易受到安慰剂悖论的影响,也被称为“健康依从者”偏见,即坚持服用安慰剂的个体似乎比不服用安慰剂的个体生存得更好。这一结果通常不是由于安慰剂的益处,而往往是不受控制的混杂的结果。在这里,我们的目的是概述使用逆概率加权对静态干预的时变暴露的生存结果进行因果推断。这里描述的基本概念也适用于其他类型的公开策略,尽管这些策略可能需要额外的设计或分析考虑。我们提供了一个研讨会指南,其中包括解决方案手册,完全可复制的R, SAS和Stata代码,以及GitHub存储库上的模拟数据集,供读者探索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal survival analysis: A guide to estimating intention-to-treat and per-protocol effects from randomized clinical trials with non-adherence
When reporting results from randomized experiments, researchers often choose to present a per-protocol effect in addition to an intention-to-treat effect. However, these per-protocol effects are often described retrospectively, for example, comparing outcomes among individuals who adhered to their assigned treatment strategy throughout the study. This retrospective definition of a per-protocol effect is often confounded and cannot be interpreted causally because it encounters treatment-confounder feedback loops, where past confounders affect future treatment, and current treatment affects future confounders. Per-protocol effects estimated using this method are highly susceptible to the placebo paradox, also called the “healthy adherers” bias, where individuals who adhere to placebo appear to have better survival than those who don’t. This result is generally not due to a benefit of placebo, but rather is most often the result of uncontrolled confounding. Here, we aim to provide an overview to causal inference for survival outcomes with time-varying exposures for static interventions using inverse probability weighting. The basic concepts described here can also apply to other types of exposure strategies, although these may require additional design or analytic considerations. We provide a workshop guide with solutions manual, fully reproducible R, SAS, and Stata code, and a simulated dataset on a GitHub repository for the reader to explore.
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