McMahan Christopher S, McLain Alexander C, Gallagher Colin M, Schisterman Enrique F
Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA.
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Biom J. 2016 Jul;58(4):944-61. doi: 10.1002/bimj.201500195. Epub 2016 Mar 1.
There is a need for epidemiological and medical researchers to identify new biomarkers (biological markers) that are useful in determining exposure levels and/or for the purposes of disease detection. Often this process is stunted by high testing costs associated with evaluating new biomarkers. Traditionally, biomarker assessments are individually tested within a target population. Pooling has been proposed to help alleviate the testing costs, where pools are formed by combining several individual specimens. Methods for using pooled biomarker assessments to estimate discriminatory ability have been developed. However, all these procedures have failed to acknowledge confounding factors. In this paper, we propose a regression methodology based on pooled biomarker measurements that allow the assessment of the discriminatory ability of a biomarker of interest. In particular, we develop covariate-adjusted estimators of the receiver-operating characteristic curve, the area under the curve, and Youden's index. We establish the asymptotic properties of these estimators and develop inferential techniques that allow one to assess whether a biomarker is a good discriminator between cases and controls, while controlling for confounders. The finite sample performance of the proposed methodology is illustrated through simulation. We apply our methods to analyze myocardial infarction (MI) data, with the goal of determining whether the pro-inflammatory cytokine interleukin-6 is a good predictor of MI after controlling for the subjects' cholesterol levels.
流行病学和医学研究人员需要识别新的生物标志物（生物学标记），这些标志物有助于确定暴露水平和/或用于疾病检测。通常，这一过程因评估新生物标志物的高昂检测成本而受阻。传统上，生物标志物评估是在目标人群中对个体进行检测。有人提出合并样本的方法来帮助降低检测成本，即将多个个体样本合并成样本池。已经开发出利用合并生物标志物评估来估计判别能力的方法。然而，所有这些程序都未能考虑混杂因素。在本文中，我们提出一种基于合并生物标志物测量的回归方法，该方法能够评估感兴趣的生物标志物的判别能力。特别是，我们开发了受试者工作特征曲线、曲线下面积和尤登指数的协变量调整估计量。我们建立了这些估计量的渐近性质，并开发了推断技术，使人们能够在控制混杂因素的同时，评估一种生物标志物是否是病例和对照之间的良好判别指标。通过模拟说明了所提出方法的有限样本性能。我们应用我们的方法分析心肌梗死（MI）数据，目的是在控制受试者胆固醇水平后，确定促炎细胞因子白细胞介素-6是否是心肌梗死的良好预测指标。
