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Positing, fitting, and selecting regression models for pooled biomarker data
Author(s) -
Mitchell Emily M.,
Lyles Robert H.,
Schisterman Enrique F.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6496
Subject(s) - akaike information criterion , pooling , computer science , model selection , parametric statistics , inference , econometrics , information criteria , bayesian information criterion , statistics , regression , data mining , machine learning , mathematics , artificial intelligence
Pooling biospecimens prior to performing lab assays can help reduce lab costs, preserve specimens, and reduce information loss when subject to a limit of detection. Because many biomarkers measured in epidemiological studies are positive and right‐skewed, proper analysis of pooled specimens requires special methods. In this paper, we develop and compare parametric regression models for skewed outcome data subject to pooling, including a novel parameterization of the gamma distribution that takes full advantage of the gamma summation property. We also develop a Monte Carlo approximation of Akaike's Information Criterion applied to pooled data in order to guide model selection. Simulation studies and analysis of motivating data from the Collaborative Perinatal Project suggest that using Akaike's Information Criterion to select the best parametric model can help ensure valid inference and promote estimate precision. Copyright © 2015 John Wiley & Sons, Ltd.