title: [Bayesian model for the risk of tuberculosis infection for studies with individuals lost to follow-up].
abstract: OBJECTIVE
  To develop a statistical model based on Bayesian methods to estimate the risk of tuberculosis infection in studies including individuals lost to follow-up, and to compare it with a classic deterministic model.
  
  
  METHODS
  The proposed stochastic model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of a longitudinal study. For simulating the unknown number of reactors at the end of the study and lost to follow-up, but not reactors at time 0, a latent variable was introduced in the new model. An exercise of application of both models in the comparison of the estimates of interest was presented.
  
  
  RESULTS
  The point estimates obtained from both models are near identical; however, the Bayesian model allowed the estimation of credible intervals as measures of precision of the estimated parameters.
  
  
  CONCLUSIONS
  The Bayesian model can be valuable in longitudinal studies with low adherence to follow-up.
authors.affiliation: Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil. edson@fmrp.usp.br
authors.forename: Edson Zangiacomi
authors.initials: EZ
authors.lastname: Martinez
authors.forename: Antonio
authors.initials: A
authors.lastname: Ruffino-Netto
authors.forename: Jorge Alberto
authors.initials: JA
authors.lastname: Achcar
authors.forename: Davi Casale
authors.initials: DC
authors.lastname: Aragon
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005500:Follow-Up Studies; D006801:Humans; D015233:Models, Statistical; D013269:Stochastic Processes; D014397:Tuberculosis, Pulmonary / Q000453:epidemiology*