Tian, Y.-Z. orcid.org/0000-0002-6173-0985, Tang, M.-L., Wong, C. orcid.org/0000-0002-5421-1458 et al. (1 more author) (2024) Bayesian analysis of joint quantile regression for multi-response longitudinal data with application to primary biliary cirrhosis sequential cohort study. Statistical Methods in Medical Research, 33 (7). pp. 1163-1184. ISSN 0962-2802
Abstract
This article proposes a Bayesian approach for jointly estimating marginal conditional quantiles of multi-response longitudinal data with multivariate mixed effects model. The multivariate asymmetric Laplace distribution is employed to construct the working likelihood of the considered model. Penalization priors on regression parameters are incorporated into the working likelihood to conduct Bayesian high-dimensional inference. Markov chain Monte Carlo algorithm is used to obtain the fully conditional posterior distributions of all parameters and latent variables. Monte Carlo simulations are conducted to evaluate the sample performance of the proposed joint quantile regression approach. Finally, we analyze a longitudinal medical dataset of the primary biliary cirrhosis sequential cohort study to illustrate the real application of the proposed modeling method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. |
Keywords: | Joint modeling; Markov chain Monte Carlo; multivariate longitudinal data; quantile regression; sequential cohort study; Bayes Theorem; Liver Cirrhosis, Biliary; Humans; Longitudinal Studies; Monte Carlo Method; Markov Chains; Algorithms; Cohort Studies; Regression Analysis; Models, Statistical; Likelihood Functions |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Arts and Humanities (Sheffield) > School of History, Philosophy and Digital Humanities |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2024 08:53 |
Last Modified: | 29 Nov 2024 08:53 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/09622802241247725 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220225 |