Abstract:
: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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Mitchell EM,Lyles RH,Schisterman EFdoi
10.1002/sim.6496subject
Has Abstractpub_date
2015-07-30 00:00:00pages
2544-58issue
17eissn
0277-6715issn
1097-0258journal_volume
34pub_type
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