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

Peter Müller, Giovanni Parmigiani, Joellen Schildkraut, Luca Tardella

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)858-866
Number of pages9
JournalBiometrics
Volume55
Issue number3
StatePublished - Sep 1999
Externally publishedYes

Keywords

  • Hierarchical model
  • Mixture
  • Ovarian cancer
  • Semiparametric Bayes

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

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