Pooling designs for outcomes under a Gaussian random effects model

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.

Figure 1

Nonsymmetric pooling designs (Designs 1, 2, 3) versus symmetric designs (Designs T,I), and random sample, where i1, i2, i3, i4 denotes the individuals and t1, t2, t3, t4 denotes the repeated measurements. R = Var_d(γ̃)/Var_T (γ̃), d= Design d. We evaluate designs with eight assays. In the random design we randomly choose eight (marked) points. (This figure appears in color in the electronic version of this article.)