Dataset Information

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

ACCESS DATA

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]

PMID: 21834127

Similar Datasets

Project description:BACKGROUND:Clinical and imaging surveillance practices following endovascular aneurysm repair (EVAR) for intact abdominal aortic aneurysm (AAA) vary considerably and compliance with recommended lifelong surveillance is poor. The aim of this study was to develop a dynamic prognostic model to enable stratification of patients at risk of future secondary aortic rupture or the need for intervention to prevent rupture (rupture-preventing reintervention) to enable the development of personalized surveillance intervals. METHODS:Baseline data and repeat measurements of postoperative aneurysm sac diameter from the EVAR-1 and EVAR-2 trials were used to develop the model, with external validation in a cohort from a single-centre vascular database. Longitudinal mixed-effects models were fitted to trajectories of sac diameter, and model-predicted sac diameter and rate of growth were used in prognostic Cox proportional hazards models. RESULTS:Some 785 patients from the EVAR trials were included, of whom 155 (19·7 per cent) experienced at least one rupture or required a rupture-preventing reintervention during follow-up. An increased risk was associated with preoperative AAA size, rate of sac growth and the number of previously detected complications. A prognostic model using predicted sac growth alone had good discrimination at 2?years (C-index 0·68), 3?years (C-index 0·72) and 5?years (C-index 0·75) after operation and had excellent external validation (C-index 0·76-0·79). More than 5?years after operation, growth rates above 1?mm/year had a sensitivity of over 80 per cent and specificity over 50 per cent in identifying events occurring within 2?years. CONCLUSION:Secondary sac growth is an important predictor of rupture or rupture-preventing reintervention to enable the development of personalized surveillance intervals. A dynamic prognostic model has the potential to tailor surveillance by identifying a large proportion of patients who may require less intensive follow-up.

Project description:BACKGROUND:Ultrasmall superparamagnetic particles of iron oxide (USPIO) detect cellular inflammation on magnetic resonance imaging (MRI). In patients with abdominal aortic aneurysm, we assessed whether USPIO-enhanced MRI can predict aneurysm growth rates and clinical outcomes. METHODS:In a prospective multicenter open-label cohort study, 342 patients with abdominal aortic aneurysm (diameter ?40 mm) were classified by the presence of USPIO enhancement and were monitored with serial ultrasound and clinical follow-up for ?2 years. The primary end point was the composite of aneurysm rupture or repair. RESULTS:Participants (85% male, 73.1±7.2 years) had a baseline aneurysm diameter of 49.6±7.7 mm, and USPIO enhancement was identified in 146 (42.7%) participants, absent in 191 (55.8%), and indeterminant in 5 (1.5%). During follow-up (1005±280 days), 17 (5.0%) abdominal aortic aneurysm ruptures, 126 (36.8%) abdominal aortic aneurysm repairs, and 48 (14.0%) deaths occurred. Compared with those without uptake, patients with USPIO enhancement have increased rates of aneurysm expansion (3.1±2.5 versus 2.5±2.4 mm/year, P=0.0424), although this was not independent of current smoking habit (P=0.1993). Patients with USPIO enhancement had higher rates of aneurysm rupture or repair (47.3% versus 35.6%; 95% confidence intervals, 1.1-22.2; P=0.0308). This finding was similar for each component of rupture (6.8% versus 3.7%, P=0.1857) or repair (41.8% versus 32.5%, P=0.0782). USPIO enhancement was associated with reduced event-free survival for aneurysm rupture or repair (P=0.0275), all-cause mortality (P=0.0635), and aneurysm-related mortality (P=0.0590). Baseline abdominal aortic aneurysm diameter (P<0.0001) and current smoking habit (P=0.0446) also predicted the primary outcome, and the addition of USPIO enhancement to the multivariate model did not improve event prediction (c-statistic, 0.7935-0.7936). CONCLUSIONS:USPIO-enhanced MRI is a novel approach to the identification of aortic wall cellular inflammation in patients with abdominal aortic aneurysms and predicts the rate of aneurysm growth and clinical outcome. However, it does not provide independent prediction of aneurysm expansion or clinical outcomes in a model incorporating known clinical risk factors. CLINICAL TRIAL REGISTRATION:URL: http://www.isrctn.com. Unique identifier: ISRCTN76413758.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets