Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.
Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.
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DOI：
10.1002/sim.6349
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发表时间：
2015-02-10
影响因子：
2
通讯作者：
Gilbert PB
中科院分区：
文献类型：
--
作者：
Gabriel EE;Sachs MC;Gilbert PB
主要替代终点可用作 I 期和 II 期试验的目标。在最近的许多试验中，测量了多种随机化后的生物标志物。然而，很少有统计方法可用于比较或组合作为主要替代物的生物标志物，并且据我们所知，这些方法都没有利用事件发生时间临床终点信息。我们提出了 Huang 和 Gilbert 的半参数估计最大似然方法的威布尔模型扩展，允许在同一风险模型中包含多个生物标志物作为多变量候选主要代理。我们提出了几种比较候选主要代理和评估多元主要代理的方法。这些包括时间依赖性和替代依赖性真阳性分数和假阳性分数、时间依赖性和综合标准化总增益以及风险差的累积分布函数。我们在模拟中说明了我们提出的方法的操作特征，并概述了如何使用这些统计数据来评估和比较候选主要代理。我们使用这些方法来调查糖尿病控制和并发症试验中的候选替代者。
Principal surrogate endpoints are useful as targets for Phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method of Huang and Gilbert that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial.