TY - JOUR

T1 - Bayesian analysis of longitudinal Johne's disease diagnostic data without a gold standard test

AU - Wang, C.

AU - Turnbull, B.W.

AU - Nielsen, Søren Saxmose

AU - Gröhn, Y.T.

PY - 2011

Y1 - 2011

N2 - A Bayesian methodology was developed based on a latent change-point model to evaluate the performance of milk ELISA and fecal culture tests for longitudinal Johne's disease diagnostic data. The situation of no perfect reference test was considered; that is, no “gold standard.” A change-point process with a Weibull survival hazard function was used to model the progression of the hidden disease status. The model adjusted for the fixed effects of covariate variables and random effects of subject on the diagnostic testing procedure. Markov chain Monte Carlo methods were used to compute the posterior estimates of the model parameters that provide the basis for inference concerning the accuracy of the diagnostic procedure. Based on the Bayesian approach, the posterior probability distribution of the change-point onset time can be obtained and used as a criterion for infection diagnosis. An application is presented to an analysis of ELISA and fecal culture test outcomes in the diagnostic testing of paratuberculosis (Johne's disease) for a Danish longitudinal study from January 2000 to March 2003. The posterior probability criterion based on the Bayesian model with 4 repeated observations has an area under the receiver operating characteristic curve (AUC) of 0.984, and is superior to the raw ELISA (AUC = 0.911) and fecal culture (sensitivity = 0.358, specificity = 0.980) tests for Johne's disease diagnosis.

AB - A Bayesian methodology was developed based on a latent change-point model to evaluate the performance of milk ELISA and fecal culture tests for longitudinal Johne's disease diagnostic data. The situation of no perfect reference test was considered; that is, no “gold standard.” A change-point process with a Weibull survival hazard function was used to model the progression of the hidden disease status. The model adjusted for the fixed effects of covariate variables and random effects of subject on the diagnostic testing procedure. Markov chain Monte Carlo methods were used to compute the posterior estimates of the model parameters that provide the basis for inference concerning the accuracy of the diagnostic procedure. Based on the Bayesian approach, the posterior probability distribution of the change-point onset time can be obtained and used as a criterion for infection diagnosis. An application is presented to an analysis of ELISA and fecal culture test outcomes in the diagnostic testing of paratuberculosis (Johne's disease) for a Danish longitudinal study from January 2000 to March 2003. The posterior probability criterion based on the Bayesian model with 4 repeated observations has an area under the receiver operating characteristic curve (AUC) of 0.984, and is superior to the raw ELISA (AUC = 0.911) and fecal culture (sensitivity = 0.358, specificity = 0.980) tests for Johne's disease diagnosis.

U2 - 10.3168/jds.2010-3675

DO - 10.3168/jds.2010-3675

M3 - Journal article

C2 - 21524521

VL - 94

SP - 2320

EP - 2328

JO - Journal of Dairy Science

JF - Journal of Dairy Science

SN - 0022-0302

IS - 5

ER -