Case-control data analysis for randomly pooled biomarkers

Neil J. Perkins, Emily M. Mitchell, Robert H. Lyles, Enrique F. Schisterman

Research output: Contribution to journalArticle

Abstract

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.

Original languageEnglish (US)
Pages (from-to)1007-1020
Number of pages14
JournalBiometrical Journal
Volume58
Issue number5
DOIs
StatePublished - Sep 1 2016
Externally publishedYes

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Keywords

  • Biomarkers
  • Gamma distribution
  • Odds ratio
  • Pooled specimens
  • Skewness

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

Cite this

Perkins, N. J., Mitchell, E. M., Lyles, R. H., & Schisterman, E. F. (2016). Case-control data analysis for randomly pooled biomarkers. Biometrical Journal, 58(5), 1007-1020. https://doi.org/10.1002/bimj.201500010