Bayesian Nonparametric Longitudinal Data Analysis.

Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.

Estimation of receiver-operating characteristic curves to determine accuracy of a competitive enzyme-linked immunosorbent assay for the serodiagnosis of Brucella infection in domestic water buffalo (Bubalus bubalis) and cattle.

OBJECTIVE: To estimate receiver-operating characteristic (ROC) curves for a competitive ELISA (c-ELISA) that is used in serodiagnosis of brucellosis in water buffalo and cattle, to determine the most appropriate positive cutoff value for the c-ELISA in confirmation of infection, and to evaluate species differences in c-ELISA function. SAMPLE POPULATION: Sera from 4 herds of cattle (n = 391) and 4 herds of water buffalo (381). PROCEDURE: Serum samples were evaluated for Brucella-specific antibodies by use of a c-ELISA. On the basis of previous serologic test results, iterative simulation modeling was used to classify animals as positive or negative for Brucella infection without the use of a gold standard. Accuracy of c-ELISA for diagnosis of infection was compared between cattle and water buffalo by comparison of areas under ROC curves. RESULTS: A positive cutoff value of 30% inhibition for c-ELISA yielded sensitivity and specificity estimates, respectively, of 83.9 and 92.6% for cattle and 91.4 and 95.4% for water buffalo. A positive cutoff value of 35% inhibition yielded sensitivity and specificity estimates, respectively, of 83.9 and 96.2% for cattle and 88.0 and 974% for water buffalo. Areas under ROC curves were 0.94 and 0.98 for cattle and water buffalo, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: ROC curves can be estimated by use of iterative simulation methods to determine optimal cutoff values for diagnostic tests with quantitative outcomes. A cutoff value of 35% inhibition for the c-ELISA was found to be most appropriate for confirmation of Brucella infection in cattle and water buffalo.

Comparison of serologic tests for detection of Brucella infections in cattle and water buffalo (Bubalus bubalis).

OBJECTIVE: To estimate sensitivity and specificity of 4 commonly used brucellosis screening tests in cattle and domestic water buffalo of Trinidad, and to compare test parameter estimates between cattle and water buffalo. ANIMALS: 391 cattle and 381 water buffalo. PROCEDURE: 4 Brucella-infected herds (2 cattle and 2 water buffalo) and 4 herds (2 of each species) considered to be brucellosis-free were selected. A minimum of 100 animals, or all animals > 1 year of age, were tested from each herd. Serum samples were evaluated for Brucella-specific antibodies by use of standard plate agglutination test (SPAT), card test (CT), buffered plate agglutination test (BPAT), and standard tube agglutination test (STAT). A Bayesian approach was used to estimate sensitivity and specificity of diagnostic tests without the use of a gold standard, assuming conditional independence of tests. RESULTS: Sensitivity and specificity estimates in cattle, respectively, were SPAT, 66.7 and 98.9; CT, 72.7 and 99.6; BPAT, 88.1 and 98.1; and STAT, 80.2 and 99.3. Corresponding test estimates in water buffalo, respectively, were SPAT, 51.4 and 99.3; CT, 90.4 and 99.4; BPAT, 96.3 and 90.7; and STAT, 75.0 and 98.8. Sensitivity of the CT and specificity of the BPAT were different between cattle and water buffalo with at least 95% probability. CONCLUSIONS AND CLINICAL RELEVANCE: Brucellosis serologic test performance varied by species tested, but BPAT had the highest sensitivity for screening cattle and water buffalo. Sensitivity and specificity of more than 2 screening tests can be estimated simultaneously without a gold standard by use of Bayesian techniques.