O'Keeffe, AG;
Su, L;
Farewell, VT;
(2018)
Correlated multistate models for multiple processes: an application to renal disease progression in systemic lupus erythematosus.
Journal of the Royal Statistical Society: Series C (Applied Statistics)
, 67
(4)
pp. 841-860.
10.1111/rssc.12257.
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Abstract
Bidirectional changes over time in the estimated glomerular filtration rate and in urine protein content are of interest for the treatment and management of patients with lupus nephritis. Although these processes may be modelled by separate multistate models, the processes are likely to be correlated within patients. Motivated by the lupus nephritis application, we develop a new multistate modelling framework where subject‐specific random effects are introduced to account for the correlations both between the processes and within patients over time. Models are fitted by using bespoke code in standard statistical software. A variety of forms for the random effects are introduced and evaluated by using the data from the Systemic Lupus International Collaborating Clinics.
| Type: | Article |
|---|---|
| Title: | Correlated multistate models for multiple processes: an application to renal disease progression in systemic lupus erythematosus |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/rssc.12257 |
| Publisher version: | https://doi.org/10.1111/rssc.12257 |
| Language: | English |
| Additional information: | Copyright © 2018 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
| Keywords: | Continuous time, Markov model, Multistate model, Multivariate longitudinal data Random effects |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices 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.ucl.ac.uk/id/eprint/10037686 |
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