Bayesian inference for two-phase studies with categorical covariates.

Abstract:

:In this article, we consider two-phase sampling in the situation in which all covariates are categorical. Two-phase designs are appealing from an efficiency perspective since they allow sampling to be concentrated in informative cells. A number of likelihood-based methods have been developed for the analysis of two-phase data, but we describe a Bayesian approach which has previously been unavailable. The methods are first compared with existing approaches via a simulation study, and are then applied to data collected on Wilms tumor. The benefits of a Bayesian approach include relaxation of the reliance on asymptotic inference, particularly in sparse data situations, and the potential to model data with complex dependencies, for example, via the introduction of random effects. The sparse data situation is illustrated via a simulated example.

journal_name

Biometrics

journal_title

Biometrics

authors

Ross M,Wakefield J

doi

10.1111/biom.12019

subject

Has Abstract

pub_date

2013-06-01 00:00:00

pages

469-77

issue

2

eissn

0006-341X

issn

1541-0420

journal_volume

69

pub_type

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