Regression for Skewed Biomarker Outcomes Subject to Pooling

• Type of Material: Article
• Authors: Emily M. Mitchell, Emory University; Robert Lyles, Emory University; Amita Manatunga, Emory University; Michelle Danaher, Eunice Kennedy Shriver National Institute of Child Health and Human Development; Neil J. Perkins, Eunice Kennedy Shriver National Institute of Child Health and Human Development; Enrique F. Schisterman, Eunice Kennedy Shriver National Institute of Child Health and Human Development
• Language: English
• Date: 2014-03
• Publisher: Wiley: Biometrics
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014, The International Biometric Society
• Final Published Version (URL): http://onlinelibrary.wiley.com/doi/10.1111/biom.12134/abstract;jsessionid=619D53D2B04E4C4E2D7B1CF01012EBA0.f01t04
• Title of Journal or Parent Work: Biometrics
• ISSN: 0006-341X
• Volume: 70
• Issue: 1
• Start Page: 202
• End Page: 211
• Grant/Funding Information: Additional partial support from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR-000454 is also acknowledged.; This research was supported by grants from the National Institute of Nursing Research (1RC4NR012527-01) and from the National Institute of Environmental Health Sciences (5R01ES012458-07).
• Keywords: Pooled specimens; Biomarkers; Efficiency; MCEM; Design; Skewness
• Research Categories: Biology, Bioinformatics; Health Sciences, Epidemiology; Biology, Biostatistics

Abstract

Summary Epidemiological studies involving biomarkers are often hindered by prohibitively expensive laboratory tests. Strategically pooling specimens prior to performing these lab assays has been shown to effectively reduce cost with minimal information loss in a logistic regression setting. When the goal is to perform regression with a continuous biomarker as the outcome, regression analysis of pooled specimens may not be straightforward, particularly if the outcome is right-skewed. In such cases, we demonstrate that a slight modification of a standard multiple linear regression model for poolwise data can provide valid and precise coefficient estimates when pools are formed by combining biospecimens from subjects with identical covariate values. When these x-homogeneous pools cannot be formed, we propose a Monte Carlo Expectation Maximization (MCEM) algorithm to compute maximum likelihood estimates (MLEs). Simulation studies demonstrate that these analytical methods provide essentially unbiased estimates of coefficient parameters as well as their standard errors when appropriate assumptions are met. Furthermore, we show how one can utilize the fully observed covariate data to inform the pooling strategy, yielding a high level of statistical efficiency at a fraction of the total lab cost.