Bayesian estimates of disease maps: How important are priors?
Statistics in Medicine1995Vol. 14(21-22), pp. 2411–2431
Citations Over TimeTop 10% of 1995 papers
Abstract
In the fully Bayesian (FB) approach to disease mapping the choice of the hyperprior distribution of the dispersion parameter is a key issue. In this context we investigated the sensitivity of the rate ratio estimates to the choice of the hyperprior via a simulation study. We also compared the performance of the FB approach to mapping disease risk to the conventional approach of mapping maximum likelihood (ML) estimates and p-values. The study was modelled on the incidence data of insulin dependent diabetes mellitus (IDDM) as observed in the communes of Sardinia.
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