Van Den Hout, ADL;
Muniz-Terrera, G;
(2018)
Hidden three-state survival model for bivariate longitudinal count data.
Lifetime Data Analysis
10.1007/s10985-018-9448-1.
(In press).
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Abstract
A model is presented that describes bivariate longitudinal count data by conditioning on a progressive illness-death process where the two living states are latent. The illness-death process is modelled in continuous time, and the count data are described by a bivariate extension of the binomial distribution. The bivariate distributions for the count data approach include the correlation between two responses even after conditioning on the state. An illustrative data analysis is discussed, where the bivariate data consist of scores on two cognitive tests, and the latent states represent two stages of underlying cognitive function. By including a death state, possible association between cognitive function and the risk of death is accounted for.
| Type: | Article |
|---|---|
| Title: | Hidden three-state survival model for bivariate longitudinal count data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10985-018-9448-1 |
| Publisher version: | https://doi.org/10.1007/s10985-018-9448-1 |
| Language: | English |
| Additional information: | © The Author(s) 2018. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Bivariate binomial distribution, Markov model, Cognitive function, Stochastic process, Latent-class model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10054833 |
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