joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes.
Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal out...
| Publicado en: | BMC Medical Research Methodology Vol. 18; no. 1 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
6/7/2018
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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=130035101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130035101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 6/7/2018 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 130035101 130035101 NLM29879902 130035101 10.1186/s12874-018-0502-1 NLM29879902 130035101 ppct: 1 formats: tig: atl: joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes. aug: au: Hickey, Graeme L. Philipson, Pete Jorgensen, Andrea Kolamunnage-Dona, Ruwanthi affil: Department of Biostatistics, Institute of Translational Medicine, University of Liverpool Waterhouse Building, 1-5 Brownlow Street L69 3GL Liverpool UK sug: subj: Algorithms Linear Regression Software Biometrics Methods Human Multivariate Analysis Reproducibility of Results Systems Analysis Prospective Studies Outcome Assessment Statistics and Numerical Data Outcome Assessment Methods Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML.Results: A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers.Conclusions: An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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