Unknown

Dataset Information

0

Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture.


ABSTRACT: Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare the shared random effects model with two approximate approaches: a naïve proportional hazards model with time-dependent covariate and a two-stage joint model, which uses plug-in estimates of the fitted values from a longitudinal analysis as covariates in a survival model. We show that the approximate approaches should be avoided since they can severely underestimate any association between the current underlying longitudinal value and the event hazard. We present classical and Bayesian implementations of the shared random effects model and highlight the advantages of the latter for making predictions. We then apply the models described to a study of abdominal aortic aneurysms (AAA) to investigate the association between AAA diameter and the hazard of AAA rupture. Out-of-sample predictions of future AAA growth and hazard of rupture are derived from Bayesian posterior predictive distributions, which are easily calculated within an MCMC framework. Finally, using a multivariate survival sub-model we show that underlying diameter rather than the rate of growth is the most important predictor of AAA rupture.

SUBMITTER: Sweeting MJ 

PROVIDER: S-EPMC3443386 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture.

Sweeting Michael J MJ   Thompson Simon G SG  

Biometrical journal. Biometrische Zeitschrift 20110810 5


Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare the shared random effects model with two approximate approaches: a naïve proportional hazards model with time-dependent covariate and a two-stage joint model, which uses plug-in e  ...[more]

Similar Datasets

| S-EPMC6175165 | biostudies-literature
| S-EPMC4767565 | biostudies-literature
| S-EPMC6355428 | biostudies-literature
2024-09-01 | GSE224587 | GEO
| S-EPMC5571881 | biostudies-literature
| S-EPMC4976321 | biostudies-literature
| S-EPMC5696466 | biostudies-literature
| S-EPMC10326087 | biostudies-literature
| S-EPMC4552620 | biostudies-literature
| S-EPMC4662083 | biostudies-literature