A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability
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| Format: | Artículo Open Access |
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Wiley
2026
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| author | Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett |
| author_facet | Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett |
| collection | Wiley Open Access |
| contents | A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett Statistics in Medicine ABSTRACT Within‐individual variability of health indicators measured over time is becoming commonly used to inform about disease progression. Simple summary statistics (e.g., the standard deviation for each individual) are often used but they are not suited to account for time changes. In addition, when these summary statistics are used as covariates in a regression model for time‐to‐event outcomes, the estimates of the hazard ratios are subject to regression dilution. To overcome these issues, a joint model is built where the association between the time‐to‐event outcome and multivariate longitudinal markers is specified in terms of the within‐individual variability of the latter. A mixed‐effect location‐scale model is used to analyze the longitudinal biomarkers, their within‐individual variability and their correlation. The time to event is modeled using a proportional hazard regression model, with a flexible specification of the baseline hazard, and the information from the longitudinal biomarkers is shared as a function of the random effects. The model can be used to quantify within‐individual variability for the longitudinal markers and their association with the time‐to‐event outcome. We show through a simulation study the performance of the model in comparison with standard joint models with constant variance. The model is applied on a dataset of adult women from the UK cystic fibrosis registry, to evaluate the association between lung function, malnutrition and mortality. 10.1002/sim.70596 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.70596 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70596 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett Statistics in Medicine A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability Marco Palma Omar El Makkaoui Ruth H. Keogh Siobhán B. Carr Rhonda Szczesniak David Taylor‐Robinson Angela M. Wood Graciela Muniz‐Terrera Jessica K. Barrett Statistics in Medicine ABSTRACT Within‐individual variability of health indicators measured over time is becoming commonly used to inform about disease progression. Simple summary statistics (e.g., the standard deviation for each individual) are often used but they are not suited to account for time changes. In addition, when these summary statistics are used as covariates in a regression model for time‐to‐event outcomes, the estimates of the hazard ratios are subject to regression dilution. To overcome these issues, a joint model is built where the association between the time‐to‐event outcome and multivariate longitudinal markers is specified in terms of the within‐individual variability of the latter. A mixed‐effect location‐scale model is used to analyze the longitudinal biomarkers, their within‐individual variability and their correlation. The time to event is modeled using a proportional hazard regression model, with a flexible specification of the baseline hazard, and the information from the longitudinal biomarkers is shared as a function of the random effects. The model can be used to quantify within‐individual variability for the longitudinal markers and their association with the time‐to‐event outcome. We show through a simulation study the performance of the model in comparison with standard joint models with constant variance. The model is applied on a dataset of adult women from the UK cystic fibrosis registry, to evaluate the association between lung function, malnutrition and mortality. 10.1002/sim.70596 http://creativecommons.org/licenses/by/4.0/ |
| title | A Bayesian Location‐Scale Joint Model for Time‐To‐Event and Multivariate Longitudinal Data With Association Based on Within‐Individual Variability |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70596 |