Continuous-data diagnostic tests for paratuberculosis as a multistage disease.
We devised a general method for interpretation of multistage diseases using continuous-data diagnostic tests. As an example, we used paratuberculosis as a multistage infection with 2 stages of infection as well as a noninfected state. Using data from a Danish research project, a fecal culture testing scheme was linked to an indirect ELISA and adjusted for covariates (parity, age at first calving, and days in milk). We used the log-transformed optical densities in a Bayesian network to obtain the probabilities for each of the 3 infection stages for a given optical density (adjusted for covariates). The strength of this approach was that the uncertainty associated with a test was imposed directly on the individual test result rather than aggregated into the population-based measures of test properties (i.e., sensitivity and specificity).
Toft
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Department of Large Animal Sciences, The Royal Veterinary and Agricultural University, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark. nt@dina.kvl.dk
Nielsen
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Jørgensen
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D000375:Aging; D000704:Analysis of Variance; D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D002418:Cattle Diseases / Q000175:diagnosis*; D004797:Enzyme-Linked Immunosorbent Assay; D005243:Feces / Q000382:microbiology; D005260:Female; D007774:Lactation; D016927:Mycobacterium avium subsp. paratuberculosis / Q000254:growth & development / Q000302:isolation & purification; D010283:Paratuberculosis / Q000175:diagnosis*; D010298:Parity; D011247:Pregnancy; D012680:Sensitivity and Specificity; D013997:Time Factors