A Bayesian hierarchical model for longtitudinal data

Senthamarikannan, K and Senthilkumar, B and Ponnuraja, C and Venkatesan, P (2010) A Bayesian hierarchical model for longtitudinal data. International Journal of Current Research, 10. pp. 12-20. ISSN 0975-833X

Archive 201055.pdf - Published Version Download (260kB)

Abstract

The paper investigates a Bayesian hierarchical model for the analysis of longitudinal data from a randomized controlled clinical tuberculosis trial. Data for each subject are observed on thirteen time point of occasions of the trial. One of the features of the data set is that observations for some variables are missing for at least one time point. In the Bayesian approach, to estimate the model, we use the Gibbs sampler, which as well allows missing data for both the response and the explanatory variables to impute at each iteration of the algorithm, given some appropriate prior distributions.

Affiliation:	ICMR-National Institute for Research in Tuberculosis
Item Type:	Article
Uncontrolled Keywords:	Bayesian hierarchical model, Longitudinal data, Gibbs sampler
Subjects:	Tuberculosis > Biostatistics
Divisions:	Statistics
Depositing User:	Dr. Rathinasabapati R
Date Deposited:	01 Jun 2022 10:45
Last Modified:	01 Jun 2022 10:45
URI:	http://eprints.nirt.res.in/id/eprint/1071

Actions (login required)

View Item