Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach.
In many areas of medical research, 'gold standard' diagnostic tests do not exist and so evaluating the performance of standardized diagnostic criteria or algorithms is problematic. In this paper we propose an approach to evaluating the operating characteristics of diagnoses using a latent class model. By defining 'true disease' as our latent variable, we are able to estimate sensitivity, specificity and negative and positive predictive values of the diagnostic test. These methods are applied to diagnostic criteria for depression using Baltimore's Epidemiologic Catchment Area Study Wave 3 data.
Johns Hopkins University School of Medicine, Division of Biostatistics, Oncology Center, Baltimore, MD 21205, USA. esg@jhu.edu
Elizabeth S
ES
Garrett
William W
WW
Eaton
Scott
S
Zeger
D015142:Baltimore; D003863:Depression / Q000175:diagnosis*; D003955:Diagnostic Tests, Routine / Q000592:standards*; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity