Pooling Designs for Outcomes under a Gaussian Random Effects Model

Yaakov Malinovsky, Paul S. Albert, Enrique F. Schisterman

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the rising cost of laboratory assays, it has become increasingly common in epidemiological studies to pool biospecimens. This is particularly true in longitudinal studies, where the cost of performing multiple assays over time can be prohibitive. In this article, we consider the problem of estimating the parameters of a Gaussian random effects model when the repeated outcome is subject to pooling. We consider different pooling designs for the efficient maximum likelihood estimation of variance components, with particular attention to estimating the intraclass correlation coefficient. We evaluate the efficiencies of different pooling design strategies using analytic and simulation study results. We examine the robustness of the designs to skewed distributions and consider unbalanced designs. The design methodology is illustrated with a longitudinal study of premenopausal women focusing on assessing the reproducibility of F2-isoprostane, a biomarker of oxidative stress, over the menstrual cycle.

Original languageEnglish (US)
Pages (from-to)45-52
Number of pages8
JournalBiometrics
Volume68
Issue number1
DOIs
StatePublished - Mar 2012
Externally publishedYes

Keywords

  • Covariance structure
  • Intraclass correlation coefficient
  • Pooling
  • Random effects model

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

Fingerprint

Dive into the research topics of 'Pooling Designs for Outcomes under a Gaussian Random Effects Model'. Together they form a unique fingerprint.

Cite this