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Putting background information about relative risks into conjugate prior distributions.

S Greenland
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Abstract

In Bayesian and empirical Bayes analyses of epidemiologic data, the most easily implemented prior specifications use a multivariate normal distribution for the log relative risks or a conjugate distribution for the discrete response vector. This article describes problems in translating background information about relative risks into conjugate priors and a solution. Traditionally, conjugate priors have been specified through flattening constants, an approach that leads to conflicts with the true prior covariance structure for the log relative risks. One can, however, derive a conjugate prior consistent with that structure by using a data-augmentation approximation to the true log relative-risk prior, although a rescaling step is needed to ensure the accuracy of the approximation. These points are illustrated with a logistic regression analysis of neonatal-death risk.

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Bayes TheoremBiometryEpidemiologic MethodsHumansInfant MortalityInfant, NewbornLogistic ModelsRegression AnalysisRiskJournal Article

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BibTeXRIS

S Greenland. 2001. Putting background information about relative risks into conjugate prior distributions.. https://doi.org/10.1111/j.0006-341x.2001.00663.x

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