Elliott Michael R, Conlon Anna S C, Li Yun, Kaciroti Nico, Taylor Jeremy M G
Department of Biostatistics, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 USA
Department of Biostatistics, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 USA.
Biostatistics. 2015 Apr;16(2):400-12. doi: 10.1093/biostatistics/kxu043. Epub 2014 Sep 17.
Because of the time and expense required to obtain clinical outcomes of interest, such as functional limitations or death, clinical trials often focus the effects of treatment on earlier and more easily obtained surrogate markers. Preliminary work to define surrogates focused on the fraction of a treatment effect "explained" by a marker in a regression model, but as notions of causality have been formalized in the statistical setting, formal definitions of high-quality surrogate markers have been developed in the causal inference framework, using either the "causal effect" or "causal association" settings. In the causal effect setting, high-quality surrogate markers have a large fraction of the total treatment effect explained by the effect of the treatment on the marker net of the treatment on the outcome. In the causal association setting, high-quality surrogate markers have large treatment effects on the outcome when there are large treatment effects on the marker, and small effects on the outcome when there are small effects on the marker. A particularly important feature of a surrogate marker is that the direction of a treatment effect be the same for both the marker and the outcome. Settings in which the marker and outcome are positively associated but the marker and outcome have beneficial and harmful or harmful and beneficial treatment effects, respectively, have been referred to as "surrogate paradoxes". If this outcome always occurs, it is not problematic; however, as correlations among the outcome, marker, and their treatment effects weaken, it may occur for some trials and not for others, leading to potentially incorrect conclusions, and real-life examples that shortened thousands of lives are unfortunately available. We propose measures for assessing the risk of the surrogate paradox using the meta-analytic causal association framework, which allows us to focus on the probability that a given treatment will yield treatment effect in different directions between the marker and the outcome, and to determine the size of a beneficial effect of the treatment on the marker required to minimize the risk of a harmful effect of the treatment on the outcome. We provide simulations and consider two applications.
由于获取诸如功能受限或死亡等感兴趣的临床结局需要耗费时间和成本，因此临床试验通常将治疗效果聚焦于更早且更易获得的替代指标。早期定义替代指标的工作聚焦于回归模型中由一个指标“解释”的治疗效果的比例，但随着因果关系的概念在统计环境中得到形式化，高质量替代指标的形式化定义已在因果推断框架中得以发展，使用的是“因果效应”或“因果关联”设定。在因果效应设定中，高质量替代指标的总治疗效果中有很大一部分是由治疗对该指标的效应（扣除治疗对结局的效应）所解释的。在因果关联设定中，当对指标有较大治疗效果时，高质量替代指标对结局有较大治疗效果；当对指标有较小治疗效果时，对结局的治疗效果也较小。替代指标的一个特别重要的特征是，治疗效果的方向对于指标和结局而言是相同的。指标和结局呈正相关，但指标和结局的治疗效果分别为有益和有害或有害和有益的情况，被称为“替代悖论”。如果这种结局总是出现，那就没有问题；然而，随着结局、指标及其治疗效果之间的相关性减弱，它可能在某些试验中出现而在其他试验中不出现，从而导致潜在的错误结论，不幸的是，有缩短数千人生命的现实例子。我们提出了使用荟萃分析因果关联框架评估替代悖论风险的方法，这使我们能够关注给定治疗在指标和结局之间产生不同方向治疗效果的概率，并确定治疗对指标的有益效果的大小，以将治疗对结局产生有害效果的风险降至最低。我们提供了模拟并考虑了两个应用。
