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Logistic regression analysis of biomarker data subject to pooling and dichotomization

Authors

  • Z. Zhang,

    Corresponding author
    • Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
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  • A. Liu,

    1. Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
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  • R.H.  Lyles,

    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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  • B. Mukherjee

    1. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
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Z. Zhang, BBB/DESPR/NICHD, 6100 Executive Boulevard, Bethesda, MD 20892-7510, USA.

E-mail: zhiwei.zhang@nih.gov

Abstract

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. Copyright © 2011 John Wiley & Sons, Ltd.

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