In this paper we present Bayesian estimators of the prevalence of tuberculosis using computational methods for simulation of samples of posterior distribution of interest. We especially considered the Gibbs sampling algorithm to generate samples of posterior distribution, and from these samples we obtained accurate inferences for the prevalence of tuberculosis. In an application, we analyzed the results of lung X-ray tests in the diagnosis of tuberculosis. With this application, we verified that Bayesian estimators are more accurate than some existing estimators usually considered by health researchers. The use of computational methods for simulation of samples as the case of the Gibbs sampling algorithm is becoming very popular for Bayesian analysis in biostatistics. These simulation techniques using the Gibbs sampling algorithm are easily implemented and do not require great computational expertise and usually can be performed using available existing software. We could also consider these techniques for studying the prevalence of other diseases.
Prevalence of tuberculosis; Bayesian analysis; Gibbs sampler
