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Optimal predictive sample size for case–control studies

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Summary. 

The identification of factors that increase the chances of a certain disease is one of the classical and central issues in epidemiology. In this context, a typical measure of the association between a disease and risk factor is the odds ratio. We deal with design problems that arise for Bayesian inference on the odds ratio in the analysis of case–control studies. We consider sample size determination and allocation criteria for both interval estimation and hypothesis testing. These criteria are then employed to determine the sample size and proportions of units to be assigned to cases and controls for planning a study on the association between the incidence of a non-Hodgkin's lymphoma and exposition to pesticides by eliciting prior information from a previous study.
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Keywords: Allocation; Association; Bayesian design; Case–control studies; Credible interval; Interval estimation; Log-normal approximation; Odds ratio; Sample size; Testing

Document Type: Research Article

Affiliations: Università di Roma “La Sapienza”, Italy

Publication date: 2004-08-01

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