A tutorial for joint modeling of longitudinal and time-to-event data in R
Author(s) / Creator(s)
Cekic, Sezen
Aichele, Stephen
Brandmaier, Andreas M.
Köhncke, Ylva
Ghisletta, Paolo
Abstract / Description
In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.
Keyword(s)
tutorial joint model mixed-effects model time-to-event association structures JMbayes package applicationPersistent Identifier
Date of first publication
2021-05-11
Journal title
Quantitative and Computational Methods in Behavioral Sciences
Volume
1
Article number
Article e2979
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Cekic, S., Aichele, S., Brandmaier, A. M., Köhncke, Y., & Ghisletta, P. (2021). A tutorial for joint modeling of longitudinal and time-to-event data in R. Quantitative and Computational Methods in Behavioral Sciences, 1, Article e2979. https://doi.org/10.5964/qcmb.2979
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qcmb.v1i1.2979.pdfAdobe PDF - 801.16KBMD5: 4855b70e17a9b3bcebed7ef7779d6cfc
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Author(s) / Creator(s)Cekic, Sezen
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Author(s) / Creator(s)Aichele, Stephen
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Author(s) / Creator(s)Brandmaier, Andreas M.
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Author(s) / Creator(s)Köhncke, Ylva
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Author(s) / Creator(s)Ghisletta, Paolo
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PsychArchives acquisition timestamp2022-04-14T11:25:26Z
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Made available on2022-04-14T11:25:26Z
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Date of first publication2021-05-11
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Abstract / DescriptionIn biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationCekic, S., Aichele, S., Brandmaier, A. M., Köhncke, Y., & Ghisletta, P. (2021). A tutorial for joint modeling of longitudinal and time-to-event data in R. Quantitative and Computational Methods in Behavioral Sciences, 1, Article e2979. https://doi.org/10.5964/qcmb.2979en_US
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ISSN2699-8432
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5739
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6343
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/qcmb.2979
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Is related tohttps://doi.org/10.23668/psycharchives.4772
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Keyword(s)tutorialen_US
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Keyword(s)joint modelen_US
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Keyword(s)mixed-effects modelen_US
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Keyword(s)time-to-eventen_US
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Keyword(s)association structuresen_US
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Keyword(s)JMbayes packageen_US
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Keyword(s)applicationen_US
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Dewey Decimal Classification number(s)150
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TitleA tutorial for joint modeling of longitudinal and time-to-event data in Ren_US
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DRO typearticle
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Article numberArticle e2979
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Journal titleQuantitative and Computational Methods in Behavioral Sciences
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Volume1
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Visible tag(s)Version of Recorden_US