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IJE Advance Access originally published online on March 24, 2004
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International Journal of Epidemiology, Volume 33, Number 3, pp. 516-517
IJE vol.33 no.3 © International Epidemiological Association 2004; all rights reserved.

Article

Commentary: Robust estimation of population parameters with sparse data

B Rachet

Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

Correspondence: Bernard Rachet, Non-Communicable Disease Epidemiology Unit, Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. E-mail: bernard.rachet@lshtm.ac.uk

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Assunção and Castro1 present a Bayesian approach based on Markov chain Monte Carlo (MCMC) methodology designed to estimate cancer incidence rates simultaneously for a number of cancers. The methodology is appropriate for providing reliable estimates when the available data are geographically and temporally sparse.

Health authorities have demanded incidence, mortality, and survival rates and other economic, socio-demographic, and health-related parameters for many years. Knowledge of geographical and temporal incidence and survival data at local level is naturally of interest to health authorities, especially if covariates such as socioeconomic status can be included in the analyses. Such statistics . . . [Full Text of this Article]


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