Estimation of prevalence on the basis of screening tests

Estimates of disease prevalence based on screening tests can be severely biased unless adjusted for the sensitivity and specificity of the screening test. One such adjusted estimate, the maximum likelihood estimator proposed by Levy and Kass, can yield an extreme estimate of zero or one that has undesirable characteristics such as a standard error of zero. We develop here a Bayesian estimator which always falls between zero and one. Users without specialized software can use the maximum likelihood estimate for most circumstances and, in special cases, such as a zero estimate of prevalence, turn to the Bayesian estimate. Others can use software to carry out a complete Bayesian solution. We have provided a method to obtain numerical values for the Bayesian estimate for those ranges of sample size (20-100), sensitivity (0.7-0.9) and specificity (0.7-0.9) for which the use of this estimator seems most practical.