title: Regression for skewed biomarker outcomes subject to pooling.
abstract: 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.
authors: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, U.S.A.
authors: Emily M
authors: EM
authors: Mitchell
authors: Robert H
authors: RH
authors: Lyles
authors: Amita K
authors: AK
authors: Manatunga
authors: Michelle
authors: M
authors: Danaher
authors: Neil J
authors: NJ
authors: Perkins
authors: Enrique F
authors: EF
authors: Schisterman
mesh_terms: D000022:Abortion, Spontaneous / Q000276:immunology; D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D018932:Chemokine CCL2 / Q000097:blood / Q000276:immunology; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D009010:Monte Carlo Method; D011247:Pregnancy
chemical_list: D015415:Biomarkers; D018932:Chemokine CCL2
keywords: Biomarkers; Design; Efficiency; MCEM; Pooled specimens; Skewness