Waymo LLC, Mountain View, California, USA.
Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
Stat Med. 2022 May 30;41(12):2227-2246. doi: 10.1002/sim.9352. Epub 2022 Feb 21.
Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.
临床研究通常需要长期随访患者和/或对关注的主要结局进行昂贵或繁琐的测量，以评估某种治疗方法的有效性。识别主要结局的替代标志物可以减少随访时间、降低成本或减轻负担，从而评估治疗效果。虽然已经进行了大量的临床和统计工作来确定和验证替代标志物，但现有的方法往往侧重于只有一个替代标志物的情况。在替代标志物数量大于样本量的高维替代标志物环境中，很少有工作来适应这种情况。在本文中，我们开发了方法来估计高维替代标志物解释治疗效果的比例。我们研究了我们提出的估计量的渐近性质，提出了推断程序，并通过模拟研究检查了有限样本性能。我们使用一项随机研究的数据说明了我们提出的方法，该研究比较了一种新型乳清基口服营养补充剂与标准补充剂在 12 周内体脂肪百分比变化方面的效果，感兴趣的替代标志物是基因表达探针组。
