Ecologic studies use data aggregated over groups rather than data on individuals. Such
studies are popular because they use existing databases and can offer large exposure …

This review examines the state of Bayesian thinking as Statistics in Medicine was launched
in 1982, reflecting particularly on its applicability and uses in medical research. It then looks …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

The R2WinBUGS package provides convenient functions to call WinBUGS from R. It
automatically writes the data and scripts in a format readable by WinBUGS for processing in …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation.
Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are …

Spatial epidemiology is the description and analysis of the geographical distribution of
disease. It is more important now than ever, with modern threats such as bio-terrorism …

In typical small‐area studies of health and environment we wish to make inference on the
relationship between individual‐level quantities using aggregate, or ecological, data. Such …

Background When analysing spatial data, it is important to account for spatial
autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the …

Inference from studies that make use of data at the level of the area, rather than at the level
of the individual, is more difficult for a variety of reasons. Some of these difficulties arise …