Bayesian joint modeling of longitudinal and spatial survival AIDS data

Martins, Rui; Silva, Giovani L.; Andreozzi, V.

doi:10.1002/sim.6937

Cited by 21 publications

(16 citation statements)

References 51 publications

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“…Linear mixed-effect model Linear Mixed-Effect (LME) models were generally used to model continuous longitudinal data [16,17,19,33,46,48,62,75,76,82], and were defined by…”

Section: Continuous Outcomementioning

confidence: 99%

“…In some cases, a Dirichlet process prior is assigned to the random effects to allow for flexibility and avoid misspecification of the random effects distribution [40]. Martins et al [82] assumed normally distributed random effects within different geographical regions to model the longitudinal outcome in a HIV study.…”

Section: Random Effect Distributionmentioning

confidence: 99%

“…Relative risk models have been used to model eventtime outcomes by Andrinopoulou et al [62], Rizopoulos and Ghosh [57], Armero et al [26] and Martins et al [82]. Andrinopoulou et al [62] used a B-spline approach for time-varying coefficients that links the longitudinal and event-time outcomes.…”

Section: Relative Risk Modelmentioning

confidence: 99%

“…Rizopoulos and Ghosh [57] employed a relative risk model to examine the risk of graft failure in study of chronic kidney disease. Martins et al [82] proposed a relative risk model to deal with spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same geographical region.…”

Section: Relative Risk Modelmentioning

confidence: 99%

“…Also, B-splines approach is utilised in many articles for specifying the baseline hazed function [22,27,37,45,46,49,56,61,81]. The baseline hazard function was also modelled parametrically by Weibull [19,23,43,50,53,82] or by using an exponential distribution [36]. Andrinopoulou et al [62] approximated the baseline hazard function using Psplines [93] and Wang et al [87] and Tang et al [28] adapted penalized splines for the baseline hazard.…”

Section: Baseline Hazard Functionmentioning

confidence: 99%

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Bayesian joint modelling of longitudinal and time to event data: a methodological review

Alsefri

Sudell

Garcı́a-Fiñana

et al. 2020

BMC Med Res Methodol

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Background: In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint models have been extensively reviewed, and due to the recent popularity of data-driven Bayesian approaches, a review on current Bayesian estimation of joint model is useful to draw recommendations for future researches. Methods: We have undertaken a comprehensive review on Bayesian univariate and multivariate joint models. We focused on type of outcomes, model assumptions, association structure, estimation algorithm, dynamic prediction and software implementation. Results: A total of 89 articles have been identified, consisting of 75 methodological and 14 applied articles. The most common approach to model the longitudinal and time-to-event outcomes jointly included linear mixed effect models with proportional hazards. A random effect association structure was generally used for linking the two sub-models. Markov Chain Monte Carlo (MCMC) algorithms were commonly used (93% articles) to estimate the model parameters. Only six articles were primarily focused on dynamic predictions for longitudinal or event-time outcomes. Conclusion: Methodologies for a wide variety of data types have been proposed; however the research is limited if the association between the two outcomes changes over time, and there is also lack of methods to determine the association structure in the absence of clinical background knowledge. Joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.

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“…Linear mixed-effect model Linear Mixed-Effect (LME) models were generally used to model continuous longitudinal data [16,17,19,33,46,48,62,75,76,82], and were defined by…”

Section: Continuous Outcomementioning

confidence: 99%

Section: Random Effect Distributionmentioning

confidence: 99%

Section: Relative Risk Modelmentioning

confidence: 99%

Section: Relative Risk Modelmentioning

confidence: 99%

Section: Baseline Hazard Functionmentioning

confidence: 99%

See 3 more Smart Citations

Bayesian joint modelling of longitudinal and time to event data: a methodological review

Alsefri

Sudell

Garcı́a-Fiñana

et al. 2020

BMC Med Res Methodol

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Bayesian Joint Models for Longitudinal and Survival Data

Pardo

2021

Wiley StatsRef: Statistics Reference Online

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This article takes a quick look at Bayesian joint models (BJMs) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects, and the prior distribution. Next, a basic BJM defined in terms of a mixed linear model and a Cox survival regression models is discussed, and some extensions and other Bayesian topics are briefly outlined.

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Joint analysis of longitudinal measurements and spatially clustered competing risks HIV/AIDS data

Momenyan

2021

Statistics in Medicine

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The joint modeling of repeated measurements and time-to-event provides a general framework to describe better the link between the progression of disease through longitudinal measurements and time-to-event outcome. In the survival data, a sample of individuals is frequently grouped into clusters. In some applications, these clusters could be arranged spatially, for example, based on geographical regions. There are two benefits of considering spatial variation in these data, enhancing the efficiency and accuracy of the parameters estimations, and investigating the survivorship spatial pattern. On the other hand, in survival data, there is a situation that subjects are supposed to experience more than one type of event potentially, but the occurrence of one type of event prevents the occurrence of the others. In this article, we considered a Bayesian joint model of longitudinal and competing risks outcomes for spatially clustered HIV/AIDS data. The data were from a registry-based study carried in Hamadan Province, Iran, from December 1997 to June 2020. In this joint model, a linear mixed effects model was used for the longitudinal submodel and a cause-specific hazard model with spatial and spatial-risk random effects was used for the survival submodel. Also, a latent structure was defined by random effects to link both event times and longitudinal processes. We used a univariate intrinsic conditional autoregressive (ICAR) distribution and a multivariate ICAR distribution for modeling the areal spatial and spatial-risk random effects, respectively. The performance of our proposed model using simulation studies and analysis of HIV/AIDS data were assessed.

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Bayesian joint modeling of longitudinal and spatial survival AIDS data

Cited by 21 publications

References 51 publications

Bayesian joint modelling of longitudinal and time to event data: a methodological review

Bayesian joint modelling of longitudinal and time to event data: a methodological review

Bayesian Joint Models for Longitudinal and Survival Data

Joint analysis of longitudinal measurements and spatially clustered competing risks HIV/AIDS data

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