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Title: | Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective | Authors: | Dutta, Srimanti MOLENBERGHS, Geert Chakraborty, Arindom |
Issue Date: | 2022 | Publisher: | TAYLOR & FRANCIS LTD | Source: | JOURNAL OF APPLIED STATISTICS, 49(9), p. 2228-2245 | Abstract: | Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed. | Notes: | Chakraborty, A (corresponding author), Visva Bharati Univ, Dept Stat, Santini Ketan, W Bengal, India. arindom.chakraborty@visva-bharati.ac.in |
Other: | Chakraborty, A (corresponding author), Visva Bharati Univ, Dept Stat, Santini Ketan, W Bengal, India. arindom.chakraborty@visva-bharati.ac.in | Keywords: | AFT model;Bartlett decomposition;Bayesian;conditional distribution;muscular dystrophy | Document URI: | http://hdl.handle.net/1942/34179 | ISSN: | 0266-4763 | e-ISSN: | 1360-0532 | DOI: | 10.1080/02664763.2021.1897971 | ISI #: | WOS:000626991600001 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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