Likelihood-based methods for regression analysis with binary exposure status assessed by pooling

• Type of Material: Article
• Authors: Robert Lyles, Emory University; Li Tang, Emory University; Ji Lin, Emory University; Zhiwei Zhang, Eunice Kennedy Shriver National Institute of Child Health and Human Development; Bhramar Mukherjee, University of Michigan
• Language: English
• Date: 2012-09-28
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 John Wiley & Sons, Ltd.
• Final Published Version (URL): http://dx.doi.org/10.1002/sim.4426
• Title of Journal or Parent Work: Statistics in Medicine
• ISSN: 0277-6715
• Volume: 31
• Issue: 22
• Start Page: 2485
• End Page: 2497
• Grant/Funding Information: Other sources of support include an RC4 grant through the National Institute of Nursing Research (1RC4NR012527-01), an R01 from the National Institute of Environmental Health Sciences (5R01ES012458-07), an RO1 from the National Cancer Institute in support of the MECC Study (1R01CA81488), and a PHS grant (UL 1 RR025008) from the Clinical and Translational Science Award Program, National Institutes of Health, Center for Research Resources.; This research was partially supported by the Long-Range Research Initiative of the American Chemistry Council (ACC) and the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health.
• Keywords: Life Sciences & Biomedicine; Public, Environmental & Occupational Health; Physical Sciences; single nucleotide polymorphism; Mathematical & Computational Biology; Science & Technology; Medicine, Research & Experimental; cross-sectional study; case-control study; repeated measures; DISEASE INCIDENCE; BIOMARKERS; Statistics & Probability; SUBJECT; Mathematics; efficiency; COLORECTAL-CANCER; Medical Informatics; logistic regression; pooling; MODELS; PREVALENCE; Research & Experimental Medicine; BIOSPECIMENS
• Research Categories: Biology, Bioinformatics; Health Sciences, Public Health; Biology, Biostatistics

Abstract

The need for resource-intensive laboratory assays to assess exposures in many epidemiologic studies provides ample motivation to consider study designs that incorporate pooled samples. In this paper, we consider the case in which specimens are combined for the purpose of determining the presence or absence of a pool-wise exposure, in lieu of assessing the actual binary exposure status for each member of the pool. We presume a primary logistic regression model for an observed binary outcome, together with a secondary regression model for exposure. We facilitate maximum likelihood analysis by complete enumeration of the possible implications of a positive pool, and we discuss the applicability of this approach under both cross-sectional and case-control sampling. We also provide a maximum likelihood approach for longitudinal or repeated measures studies where the binary outcome and exposure are assessed on multiple occasions and within-subject pooling is conducted for exposure assessment. Simulation studies illustrate the performance of the proposed approaches along with their computational feasibility using widely available software. We apply the methods to investigate gene-disease association in a population-based case-control study of colorectal cancer.