Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892-7510, USA.
Stat Med. 2012 Sep 28;31(22):2473-84. doi: 10.1002/sim.4367. Epub 2011 Sep 23.
There is growing interest in pooling specimens across subjects in epidemiologic studies, especially those involving biomarkers. This paper is concerned with regression analysis of epidemiologic data where a binary exposure is subject to pooling and the pooled measurement is dichotomized to indicate either that no subjects in the pool are exposed or that some are exposed, without revealing further information about the exposed subjects in the latter case. The pooling process may be stratified on the disease status (a binary outcome) and possibly other variables but is otherwise assumed random. We propose methods for estimating parameters in a prospective logistic regression model and illustrate these with data from a population-based case-control study of colorectal cancer. Simulation results show that the proposed methods perform reasonably well in realistic settings and that pooling can lead to sizable gains in cost efficiency. We make recommendations with regard to the choice of design for pooled epidemiologic studies.
人们对在流行病学研究中对受试者的样本进行汇总越来越感兴趣，特别是那些涉及生物标志物的研究。本文主要关注在流行病学数据的回归分析中，当二元暴露受到汇总并且汇总的测量结果被二分类为表示池中没有受试者暴露或某些受试者暴露，而在后一种情况下不透露有关暴露受试者的进一步信息。汇总过程可以按疾病状态（二项结局）和可能的其他变量分层，但在其他方面被认为是随机的。我们提出了在前瞻性逻辑回归模型中估计参数的方法，并通过结直肠癌的基于人群的病例对照研究的数据说明了这些方法。模拟结果表明，所提出的方法在现实环境中表现良好，并且汇总可以大大提高成本效率。我们就汇总流行病学研究的设计选择提出了建议。
