An Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference
An Information-Theoretic Approach for the Evaluation of Surrogate Endpoints Based on Causal Inference
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DOI：
10.1111/biom.12483
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发表时间：
2016-09-01
期刊：
影响因子：
1.9
通讯作者：
Burzykowski, Tomasz
中科院分区：
文献类型：
--
作者：
Alonso, Ariel;Van der Elst, Wim;Burzykowski, Tomasz
在这项工作中，一个新的指标的代孕，所谓的个人因果关联（伊卡），介绍了使用信息理论的概念和因果推理模型的二元代理和真正的端点。伊卡在减少不确定性方面有一个简单而有吸引力的解释，在某些情况下，它似乎比现有的措施提供了一个更一致的评估替代品的有效性。可识别性问题的解决使用两个步骤的程序。在第一步中，与手头的数据兼容的潜在结果的分布的参数空间的区域被几何地表征。此外，在第二步中，提出了蒙特卡罗方法来研究伊卡在前一区域上的行为。该方法说明使用的数据从合作初始青光眼治疗研究。一个新开发的和用户友好的R包替代提供进行评估工作。
In this work a new metric of surrogacy, the so-called individual causal association (ICA), is introduced using information-theoretic concepts and a causal inference model for a binary surrogate and true endpoint. The ICA has a simple and appealing interpretation in terms of uncertainty reduction and, in some scenarios, it seems to provide a more coherent assessment of the validity of a surrogate than existing measures. The identifiability issues are tackled using a two-step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study. A newly developed and user-friendly R package Surrogate is provided to carry out the evaluation exercise.