Pooling Designs for Outcomes under a Gaussian Random Effects Model

Authors

  • Yaakov Malinovsky,

    Corresponding author
    1. Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland 20892, U.S.A.
    2. Current address: Department of Mathematics and Statistics University of Maryland Baltimore County, Baltimore, Maryland 21250, U.S.A.
      email: yaakovm@umbc.edu
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  • Paul S. Albert,

    1. Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland 20892, U.S.A.
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  • Enrique F. Schisterman

    1. Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland 20892, U.S.A.
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email: yaakovm@umbc.edu

Abstract

Summary 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.

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