Positing, fitting, and selecting regression models for pooled biomarker data

Emily M. Mitchell, Robert H. Lyles, Enrique F. Schisterman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2544-2558
Number of pages15
JournalStatistics in Medicine
Issue number17
StatePublished - Jul 30 2015
Externally publishedYes


  • AIC
  • Biomarkers
  • Gamma
  • MCEM
  • Pooled specimens
  • Skewness

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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