title: Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation.
abstract: Two-phase study designs are appealing since they allow for the oversampling of rare sub-populations which improves efficiency. In this paper we describe a Bayesian hierarchical model for the analysis of two-phase data. Such a model is particularly appealing in a spatial setting in which random effects are introduced to model between-area variability. In such a situation, one may be interested in estimating regression coefficients or, in the context of small area estimation, in reconstructing the population totals by strata. The efficiency gains of the two-phase sampling scheme are compared to standard approaches using 2011 birth data from the research triangle area of North Carolina. We show that the proposed method can overcome small sample difficulties and improve on existing techniques. We conclude that the two-phase design is an attractive approach for small area estimation.
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
authors.forename: Michelle
authors.initials: M
authors.lastname: Ross
authors.affiliation: Departments of Biostatistics and Statistics, University of Washington, Seattle, WA USA.
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
keywords: Bayesian hierarchical model; Markov chain Monte Carlo; Outcome-dependent sampling; Small area estimation