Spatio-temporal interaction with disease mapping.

Markov chain Monte Carlo methods are used to estimate mortality rates under a Bayesian hierarchical model. Spatial correlations are introduced to examine spatial effects relative to both regional and regional changes over time by groups. A special feature of the models is the inclusion of longitudinal variables which will describe temporal trends in mortality or incidences for different population groups.

Bayesian design for dose-response curves with penalized risk.

This paper considers some Bayesian design problems in quantal response analysis. An experimenter must choose a set of dose levels and number of independent observations to take at these levels, subject to a total sample size, in order to estimate some characteristic phi, e.g.

Analysis of mortality rates via marginal extended quasi-likelihood.

We use a mixed Poisson regression model with extra variation to analyse mortality data cross-classified by age and geographic region. We use estimates of dispersion parameter and fixed effects parameters, obtained by maximizing a marginal quasi-likelihood function, to estimate mortality rates in an empirical Bayes manner. This is a modification of an earlier method by Tsutakawa that used the likelihood function and is computationally impractical for routine use.

The relationship between neonatal mortality and hospital level.

Background: The relative safety of the small obstetrics unit compared with that of the larger or more technologically sophisticated units remains controversial. The purpose of this study was to examine the relationship between neonatal mortality and the level of perinatal services present in the hospital of birth.

Methods: Logistic regression was used to model neonatal mortality as a function of race, weight, and hospital level.

Mixed model for analyzing geographic variability in mortality rates.

"A mixed model is proposed for the analysis of geographic variability in mortality rates. In addition to demographic parameters and random geographic parameters, the model includes additional random-effects parameters to adjust for extra-Poisson variability. The model uses a gamma-Poisson distribution with a random scale parameter having an inverse gamma prior.