Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data

Tang, An Min; Zhao, Xingqiu; Tang, Nian

doi:10.1002/bimj.201500070

Cited by 14 publications

(22 citation statements)

References 54 publications

(118 reference statements)

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“…So far model selection is conducted via DIC. We note that more advanced model selection techniques such as Bayesian Lasso selection (Tang, Zhao, & Tang, 2017) or boosting (Waldmann et al, 2017) have been developed. Including these techniques into the presented framework are topics for future work.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Flexible Bayesian additive joint models with an application to type 1 diabetes research

et al. 2017

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The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Using Bayesian P-splines, we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying association between the marker and event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R-package bamlss.

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Section: Discussion and Outlookmentioning

confidence: 99%

Flexible Bayesian additive joint models with an application to type 1 diabetes research

et al. 2017

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“…Multiple longitudinal outcomes were considered in 19 articles. Eight presented methods where all longitudinal outcomes were the same type of data (continuous outcomes [21,28,63], count outcomes [50], or ordinal outcomes [26]) whilst other 11 articles presented methods when the longitudinal outcomes were a mix of data types (e.g. continuous, ordinal and binary longitudinal outcomes [30]).…”

Section: Multivariate Longitudinal Outcomes (K > 1)mentioning

confidence: 99%

“…For continuous data, generally multivariate mixed effect models were used [21,28,63,83] and were described as in (1) for each k. The model accounted for two sources of dependency; within-individual repeated measurements over time for a given longitudinal outcome and between different longitudinal outcomes for the same individual.…”

Section: Continuous Outcomesmentioning

confidence: 99%

“…Six (9.5%) methodological articles considered joint models for multiple event outcomes. The Cox proportional hazards model was commonly used to model the multiple event-times [14,28,32,34,38].…”

Section: Multiple Event-time Outcomesmentioning

confidence: 99%

“…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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Semiparametric normal transformation joint model of multivariate longitudinal and bivariate time‐to‐event data

Tang,

Peng,

Tang

2023

Statistics in Medicine

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Joint models for longitudinal and survival data (JMLSs) are widely used to investigate the relationship between longitudinal and survival data in clinical trials in recent years. But, the existing studies mainly focus on independent survival data. In many clinical trials, survival data may be bivariately correlated. To this end, this paper proposes a novel JMLS accommodating multivariate longitudinal and bivariate correlated time‐to‐event data. Nonparametric marginal survival hazard functions are transformed to bivariate normal random variables. Bayesian penalized splines are employed to approximate unknown baseline hazard functions. Incorporating the Metropolis‐Hastings algorithm into the Gibbs sampler, we develop a Bayesian adaptive Lasso method to simultaneously estimate parameters and baseline hazard functions, and select important predictors in the considered JMLS. Simulation studies and an example taken from the International Breast Cancer Study Group are used to illustrate the proposed methodologies.

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Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data

Cited by 14 publications

References 54 publications

Flexible Bayesian additive joint models with an application to type 1 diabetes research

Flexible Bayesian additive joint models with an application to type 1 diabetes research

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

Semiparametric normal transformation joint model of multivariate longitudinal and bivariate time‐to‐event data

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