September 2022 Joint mixture quantile regressions and time-to-event analysis
Getachew A. Dagne
Author Affiliations +
Braz. J. Probab. Stat. 36(3): 492-503 (September 2022). DOI: 10.1214/22-BJPS537

Abstract

Growth curve mixture models for longitudinal data are often developed on the conditional mean of a response, focusing only on the central section of the distribution. There is, however, an increasing desire to provide holistic information on different parts of the distribution of the response such as lower and higher quantiles. This article presents quantile regression analysis within the framework of growth curve models by jointly analyzing time to an event and longitudinal data with multiphasic features. The multiphasic patterns are accounted for at different quantiles by modeling heterogeneous growth trajectories which show gradual changes from a declining trend to an increasing trend over time within latent classes. Thus, we assess these important features of longitudinal data using bent-cable models along with a joint modeling of time to event process and response process. The proposed methods are illustrated using a real data set from an AIDS clinical study.

Citation

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Getachew A. Dagne. "Joint mixture quantile regressions and time-to-event analysis." Braz. J. Probab. Stat. 36 (3) 492 - 503, September 2022. https://doi.org/10.1214/22-BJPS537

Information

Received: 1 July 2021; Accepted: 1 April 2022; Published: September 2022
First available in Project Euclid: 26 September 2022

MathSciNet: MR4489178
zbMATH: 1496.62168
Digital Object Identifier: 10.1214/22-BJPS537

Keywords: Bayesian inference , change-point model , latent variable , Survival analysis

Rights: Copyright © 2022 Brazilian Statistical Association

Vol.36 • No. 3 • September 2022
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