Liu Yan, McMahan Christopher, Gallagher Colin
Department of Mathematical Sciences, Clemson University, Clemson, 29634, SC, U.S.A.
Stat Med. 2017 Jul 10;36(15):2363-2377. doi: 10.1002/sim.7291. Epub 2017 Mar 28.
As a cost-efficient data collection mechanism, the process of assaying pooled biospecimens is becoming increasingly common in epidemiological research; for example, pooling has been proposed for the purpose of evaluating the diagnostic efficacy of biological markers (biomarkers). To this end, several authors have proposed techniques that allow for the analysis of continuous pooled biomarker assessments. Regretfully, most of these techniques proceed under restrictive assumptions, are unable to account for the effects of measurement error, and fail to control for confounding variables. These limitations are understandably attributable to the complex structure that is inherent to measurements taken on pooled specimens. Consequently, in order to provide practitioners with the tools necessary to accurately and efficiently analyze pooled biomarker assessments, herein, a general Monte Carlo maximum likelihood-based procedure is presented. The proposed approach allows for the regression analysis of pooled data under practically all parametric models and can be used to directly account for the effects of measurement error. Through simulation, it is shown that the proposed approach can accurately and efficiently estimate all unknown parameters and is more computational efficient than existing techniques. This new methodology is further illustrated using monocyte chemotactic protein-1 data collected by the Collaborative Perinatal Project in an effort to assess the relationship between this chemokine and the risk of miscarriage. Copyright © 2017 John Wiley & Sons, Ltd.
作为一种经济高效的数据收集机制，检测混合生物样本的过程在流行病学研究中越来越普遍；例如，为了评估生物标志物的诊断效能，有人提出了样本合并的方法。为此，几位作者提出了一些技术，可用于分析连续的合并生物标志物评估。遗憾的是，这些技术大多是在严格的假设下进行的，无法考虑测量误差的影响，也无法控制混杂变量。可以理解，这些局限性是由于对合并样本进行测量时固有的复杂结构所致。因此，为了为从业者提供准确、高效地分析合并生物标志物评估所需的工具，本文提出了一种基于蒙特卡罗最大似然法的通用程序。所提出的方法允许在几乎所有参数模型下对合并数据进行回归分析，并可用于直接考虑测量误差的影响。通过模拟表明，所提出的方法能够准确、高效地估计所有未知参数，并且比现有技术计算效率更高。使用围产期协作项目收集的单核细胞趋化蛋白-1数据进一步说明了这种新方法，以评估这种趋化因子与流产风险之间的关系。版权所有© 2017约翰威立父子有限公司。
