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dc.contributor.authorGasparini, Alessandro
dc.contributor.authorAbrams, Keith R
dc.contributor.authorBarrett, Jessica K
dc.contributor.authorMajor, Rupert W
dc.contributor.authorSweeting, Michael J
dc.contributor.authorBrunskill, Nigel J
dc.contributor.authorCrowther, Michael J
dc.date.accessioned2020-02-03T01:46:31Z
dc.date.available2020-02-03T01:46:31Z
dc.date.issued2019-09-05
dc.identifier.issn0039-0402
dc.identifier.otherPMC6919310
dc.identifier.other31894164
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301624
dc.description.abstractElectronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 9876667
dc.subjectLongitudinal data
dc.subjectMonte Carlo simulation
dc.subjectSelection Bias
dc.subjectElectronic Health Records
dc.subjectMixed‐effects Models
dc.subjectInformative Visiting Process
dc.subjectInverse Intensity Of Visiting Weighting
dc.subjectRecurrent‐events Models
dc.titleMixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.
dc.typeArticle
dc.date.updated2020-02-03T01:46:30Z
prism.publicationNameStatistica Neerlandica, volume 74, issue 1
dc.identifier.doi10.17863/CAM.48694
rioxxterms.versionofrecord10.1111/stan.12188
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidGasparini, Alessandro [0000-0002-8319-7624]


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International