Bayesian estimation for performance measures of two diagnostic tests in the presence of verification bias.
Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.
Aragon
Davi Casale
DC
Department of Social Medicine, Medical School of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil.
Martinez
Edson Zangiacomi
EZ
Achcar
Jorge Alberto
JA
D000465:Algorithms; D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D003955:Diagnostic Tests, Routine / Q000379:methods* / Q000592:standards; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D030361:Papillomavirus Infections / Q000175:diagnosis; D018401:Sample Size; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis; D014626:Vaginal Smears