Bayesian Hierarchical Models for Smoothing in Two-Phase Studies, with Application to Small-Area Estimation

• Activity Number: 483
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Michelle Ross* and Jon Wakefield
• Companies: University of Pennsylvania and University of Washington
• Keywords: Bayesian Hierarchical Model ; Small Area Estimation ; Two-phase design ; Outcome dependent sampling ; Markov chain Monte Carlo

Abstract

We consider the use of two-phase study designs in the context of small area estimation, where the goal is to reconstruct (unobserved) populations total by strata of interest. We describe a Bayesian hierarchical model that includes both spatial and non-spatial random effects to account for confounding by location. The methods are illustrated using birth data from North Carolina. For these data, we construct several survey sampling schemes and two-phase designs in order to compare the efficiency of standard survey approaches with two-phase approaches in both parameter and small area estimation. We show that the count reconstruction can be carried out accurately based on a fraction of the original data.