Journal of Biometrics & Biostatistics

Bayesian Mixed-effects Polychotomous Response Model with Application to Diverse Population Collaboration (DPC) Data

Abstract

Fang Yang, Xu-Feng Niu and Jianchang Lin

Polychotomous response models are commonly used in the clinical trials to analyze categorical or ordinal response data. Motivated by investigating of relationship between BMI categories and several risk factors, we carry out the application studies to examine the impact of risk factors on BMI categories, especially for categories of “Overweight” and “Obesities”. In this study, we apply the Bayesian methodology through a mixed-effects polychotomous response model to the Diverse Population Collaboration (DPC) dataset. Using the mixed-effects Bayesian polychotomous response model with uniform improper priors, we would get similar interpretations of the association between risk factors and BMI, which are in great agreement with the results documented in literature. Our application showed that the Bayesian mixed-effects polychotomous response model with improper priors is a very useful statistical technique for solving real word problems.