Bayesian sample size determination for case-control studies with misclassification

Case-control studies are among the most commonly used means of assessing association between exposure and outcome. Sample size determination and the optimal control-to-case ratio are vital to the design of such studies. In this article we investigate Bayesian sample size determination and the control-to-case ratio for case-control studies, when interval estimation is the goal of the eventual statistical analysis. In certain cases we are able to derive approximate closed-form sample size formulas. We also describe two Monte Carlo methods, each of which provides a unified approach to the sample size problem, because they may be applied to a wide range of interval-based criteria. We compare the accuracy of the different methods. We also extend our methods to include cross-sectional designs and designs for gene–environment interaction studies.