alexa Bayesian Mixed-effects Polychotomous Response Model wit
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

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

Fang Yang1, Xu-Feng Niu2 and Jianchang Lin3*

1Novartis Pharmaceuticals, Cambridge, MA 02139, USA

2Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA

3Takeda Pharmaceuticals, Cambridge, MA 02139, USA

*Corresponding Author:
Yang F
Novartis Pharmaceuticals
Cambridge, MA 02139, USA
Tel: (850) 228-7421
E-mail: [email protected]

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.

 

Abstract

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.

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