An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay.

ABSTRACT: Potential reductions in laboratory assay costs afforded by pooling equal aliquots of biospecimens have long been recognized in disease surveillance and epidemiological research and, more recently, have motivated design and analytic developments in regression settings. For example, Weinberg and Umbach (1999, Biometrics 55, 718-726) provided methods for fitting set-based logistic regression models to case-control data when a continuous exposure variable (e.g., a biomarker) is assayed on pooled specimens. We focus on improving estimation efficiency by utilizing available subject-specific information at the pool allocation stage. We find that a strategy that we call "(y,c)-pooling," which forms pooling sets of individuals within strata defined jointly by the outcome and other covariates, provi

SUBMITTER: Lyles RH 

PROVIDER: S-EPMC5014596 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature