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A Bayesian Hierarchical Approach for Combining Case‐Control and Prospective Studies
Author(s) -
Muller Peter,
Parmigiani Giovanni,
Schildkraut Joellen,
Tardella Luca
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00858.x
Subject(s) - covariate , computer science , markov chain monte carlo , bayesian probability , context (archaeology) , inference , gibbs sampling , econometrics , machine learning , data mining , statistics , artificial intelligence , mathematics , paleontology , biology
Summary. 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 casecontrol 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.