Related Causal Frameworks for Surrogate Outcomes
Related Causal Frameworks for Surrogate Outcomes
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DOI：
10.1111/j.1541-0420.2008.01106.x
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发表时间：
2009-06-01
期刊：
影响因子：
1.9
通讯作者：
Greene, Tom
中科院分区：
文献类型：
--
作者：
Joffe, Marshall M.;Greene, Tom
四个主要的框架已经开发出在随机试验中评估替代标记物：一个基于可观察变量的条件独立性，另一个基于直接和间接效应，第三个基于荟萃分析，第四个基于主要分层。其中前两个符合我们称之为因果效应（CE）范式的范式，其中，对于一个好的替代品，治疗对替代品的影响，结合替代品对临床结果的影响，可以预测治疗对临床结果的影响。最后两种方法属于脑血管相关（CA）范式，其中治疗对替代物的影响与其对临床结果的影响相关。我们首先考虑CE范式，并考虑识别假设和一些简单的估计程序，然后我们考虑CA范式。我们研究这些方法和相关的估计之间的关系。我们进行了一个小的模拟研究，以说明在不同的情况下，各种估计的属性，并得出结论，讨论这两种范式的适用性。
Four major frameworks have been developed for evaluating surrogate markers in randomized trials: one based on conditional independence of observable variables, another based on direct and indirect effects, a third based on a meta-analysis, and a fourth based on principal stratification. The first two of these fit into a paradigm we call the causal-effects (CE) paradigm, in which, for a good surrogate, the effect of treatment on the surrogate, combined with the effect of the surrogate on the clinical outcome, allow prediction of the effect of the treatment on the clinical outcome. The last two approaches fall into the causal-association (CA) paradigm, in which the effect of the treatment on the surrogate is associated with its effect on the clinical outcome. We consider the CE paradigm first, and consider identifying assumptions and some simple estimation procedures; we then consider the CA paradigm. We examine the relationships among these approaches and associated estimators. We perform a small simulation study to illustrate properties of the various estimators under different scenarios, and conclude with a discussion of the applicability of both paradigms.