Bayesian joint modelling of longitudinal and time to event data: a methodological review.
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...
| Publicado en: | BMC Medical Research Methodology Vol. 20; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/26/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142908163&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142908163 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 4/26/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 142908163 142908163 NLM32336264 142908163 10.1186/s12874-020-00976-2 NLM32336264 142908163 ppf: 1 ppct: 17 formats: tig: atl: Bayesian joint modelling of longitudinal and time to event data: a methodological review. aug: au: Alsefri, Maha Sudell, Maria García-Fiñana, Marta Kolamunnage-Dona, Ruwanthi affil: Department of Health Data Science, Institute of Population Health, University of Liverpool, L69 3GL, Liverpool, UK sug: subj: Probability Systems Analysis Human Linear Regression Prospective Studies Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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