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Approximate hierarchical modelling of discrete data in epidemiology

Department of Biostatistics, University of Washington, Seattle, Washington, USA, norm{at}biostat.washington.edu

Brian Leroux

Department of Biostatistics, University of Washington, Seattle, Washington, USA

Robert Platt

Montreal Children's Hospital Research Institute, McGill University, Montreal, Canada

Hierarchical models are used in epidemiology to estimate and analyse multiple, related relative risks. Examples include meta-analyses of series of 2 x 2 tables and mapping of spatially correlated disease rates. Empirical transform and penalized quasilikelihood procedures, both of which may be implemented using standard programs for mixed model analysis, provide satisfactory approximate inferences for these problems when cell frequencies are large. Simulation studies show that, in certain situations involving small cell frequencies, penalized quasilikelihood provides satisfactory estimates of variance components and regression coefficients whereas the empirical transform approach does not.

Statistical Methods in Medical Research, Vol. 7, No. 1, 49-62 (1998)
DOI: 10.1177/096228029800700105


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