期刊
STATISTICS IN MEDICINE
卷 34, 期 17, 页码 2544-2558出版社
WILEY
DOI: 10.1002/sim.6496
关键词
AIC; biomarkers; gamma; MCEM; pooled specimens; skewness
类别
资金
- Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health
- Long-Range Research Initiative of the American Chemistry Council
- National Institute of Nursing Research, the National Institute of Environmental Health Sciences [5R01ES012458-07]
- National Center for Advancing Translational Sciences of the National Institutes of Health [UL1TR000454]
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 (C) 2015 John Wiley & Sons, Ltd.
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