A Bayesian hierarchical approach for combining case-control and prospective studies.

Published

Journal Article

Motivated by the absolute risk predictions required in medical decision making and patient counseling, we propose an approach for the combined analysis of case-control and prospective studies of disease risk factors. The approach is hierarchical to account for parameter heterogeneity among studies and among sampling units of the same study. It is based on modeling the retrospective distribution of the covariates given the disease outcome, a strategy that greatly simplifies both the combination of prospective and retrospective studies and the computation of Bayesian predictions in the hierarchical case-control context. Retrospective modeling differentiates our approach from most current strategies for inference on risk factors, which are based on the assumption of a specific prospective model. To ensure modeling flexibility, we propose using a mixture model for the retrospective distributions of the covariates. This leads to a general nonlinear regression family for the implied prospective likelihood. After introducing and motivating our proposal, we present simple results that highlight its relationship with existing approaches, develop Markov chain Monte Carlo methods for inference and prediction, and present an illustration using ovarian cancer data.

Full Text

Duke Authors

Cited Authors

  • Müller, P; Parmigiani, G; Schildkraut, J; Tardella, L

Published Date

  • September 1999

Published In

Volume / Issue

  • 55 / 3

Start / End Page

  • 858 - 866

PubMed ID

  • 11315018

Pubmed Central ID

  • 11315018

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.0006-341x.1999.00858.x

Language

  • eng

Conference Location

  • United States