Bayesian Mixed-effects Polychotomous Response Model with Application to Diverse Population Collaboration (DPC) DataFang Yang1, Xu-Feng Niu2 and Jianchang Lin3*
- *Corresponding Author:
- Yang F
Cambridge, MA 02139, USA
Tel: (850) 228-7421
Received Date: April 20, 2017; Accepted Date: April 25, 2017; Published Date: April 28, 2017
Citation: Yang F, Niu XF, Lin J (2017) Bayesian Mixed-effects Polychotomous Response Model with Application to Diverse Population Collaboration (DPC) Data. J Biom Biostat 8: 346. doi: 10.4172/2155-6180.1000346
Copyright: © 2017 Yang F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.