A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time‐to‐event

Rizopoulos, Dimitris; Ghosh, Pulak

doi:10.1002/sim.4205

Cited by 188 publications

(261 citation statements)

References 37 publications

(40 reference statements)

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“…In order to check the fit of the survival model taking into account the censoring times, we can graphically compare the associated Kaplan-Meier estimate for r CS ik (T ik ) with the survival function of the unit exponential distribution [36].…”

Section: Residual Analysismentioning

confidence: 99%

Bayesian joint modeling of longitudinal and spatial survival AIDS data

Martins

Silva

Andreozzi

2016

Statistics in Medicine

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Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with nonlinear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years [2002][2003][2004][2005][2006]. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those ones obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states.

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Section: Residual Analysismentioning

confidence: 99%

Bayesian joint modeling of longitudinal and spatial survival AIDS data

Martins

Silva

Andreozzi

2016

Statistics in Medicine

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“…Other variables like the result of digital rectal exam and transuretral ultrasound were not measured systematically but only when a biopsy was performed. The recent approach of [28] on multivariate longitudinal joint models should be considered in future works on individual risk assessment.…”

Section: Discussionmentioning

confidence: 99%

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

Serrat

Rué

Pardo

et al. 2015

Journal of Applied Statistics

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The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer (ERSPC) study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.

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“…There are also cases where timedependent Cox models are appropriate [21]. Furthermore, although some specialized methods have been proposed for joint modeling with multiple longitudinal covariates, including conditional score estimator [22], latent class approach [23], and Bayesian methods [24,25], the computational methods and software for multivariate joint modeling are not fully developed. For these reasons, the time-dependent Cox model was used in this work.…”

Section: Models For Risk Predictionmentioning

confidence: 98%