Robust estimation of the proportion of treatment effect explained by surrogate marker information.
Robust estimation of the proportion of treatment effect explained by surrogate marker information.
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DOI：
10.1002/sim.6820
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发表时间：
2016-05-10
影响因子：
2
通讯作者：
Tian L
中科院分区：
文献类型：
--
作者：
Parast L;McDermott MM;Tian L
在主要结局需要长期随访患者和/或昂贵或侵入性获取程序的随机治疗研究中，可用于估计治疗效果并可能在主要结局之前观察到的替代标志物的可用性将使研究人员能够在所需随访时间和资源较少的情况下得出有关治疗效果的结论。有效替代标志物的普伦蒂斯标准要求对替代标志物的治疗效果的检验也是对关注的主要结局的治疗效果的有效检验。基于这一标准，已开发出方法来定义和估计治疗效应对主要结局的比例，该比例可通过对替代标志物的治疗效应来解释。这些方法的目的是确定有用的统计替代品，捕获大部分的治疗效果。然而，目前估计这一比例的方法通常需要限制性的模型假设，这些假设在实践中可能不成立，因此可能导致对这一数量的有偏估计。在本文中，我们提出了一个非参数的程序来估计治疗效果的主要结果，这是解释的治疗效果的一个潜在的替代标志物的比例，并扩展此程序的设置与多个替代标志物。我们比较我们的方法，以前提出的基于模型的方法，并提出了一个方差估计过程的基础上的扰动reservation方法。仿真研究表明，该程序在有限样本中表现良好，当指定的模型不正确时，优于基于模型的程序。我们说明了我们提出的程序使用的数据集从一个随机研究调查组介导的认知行为干预外周动脉疾病的参与者。
In randomized treatment studies where the primary outcome requires long follow-up of patients and/or expensive or invasive obtainment procedures, the availability of a surrogate marker that could be used to estimate the treatment effect and could potentially be observed earlier than the primary outcome would allow researchers to make conclusions regarding the treatment effect with less required follow-up time and resources. The Prentice criterion for a valid surrogate marker requires that a test for treatment effect on the surrogate marker also be a valid test for treatment effect on the primary outcome of interest. Based on this criterion, methods have been developed to define and estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. These methods aim to identify useful statistical surrogates that capture a large proportion of the treatment effect. However, current methods to estimate this proportion usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. In this paper, we propose a nonparametric procedure to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on a potential surrogate marker and extend this procedure to a setting with multiple surrogate markers. We compare our approach to previously proposed model-based approaches and propose a variance estimation procedure based on a perturbation-resampling method. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using a dataset from a randomized study investigating a group-mediated cognitive behavioral intervention for peripheral artery disease participants.