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A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a multidrug‐resistant tuberculosis (MDR‐TB) study

Kang, Jian, Zhang, Nanhua, Shi, Ran
Biometrics 2014 v.70 no.4 pp. 981-992
Markov chain, algorithms, biometry, case studies, multiple drug resistance, regression analysis, simulation models, tuberculosis
There has been an increasing interest in the analysis of spatially distributed multivariate binary data motivated by a wide range of research problems. Two types of correlations are usually involved: the correlation between the multiple outcomes at one location and the spatial correlation between the locations for one particular outcome. The commonly used regression models only consider one type of correlations while ignoring or modeling inappropriately the other one. To address this limitation, we adopt a Bayesian nonparametric approach to jointly modeling multivariate spatial binary data by integrating both types of correlations. A multivariate probit model is employed to link the binary outcomes to Gaussian latent variables; and Gaussian processes are applied to specify the spatially correlated random effects. We develop an efficient Markov chain Monte Carlo algorithm for the posterior computation. We illustrate the proposed model on simulation studies and a multidrug‐resistant tuberculosis case study.