A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R
Abstract: 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 .... https://qcmb.psychopen.eu/index.php/qcmb/article/view/2979
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R ; volume:1 ; number:1 ; day:11 ; month:05 ; year:2021
Quantitative and computational methods in behavioral sciences ; 1, Heft 1 (11.05.2021)
- Creator
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Cekic, Sezen
Aichele, Stephen
Brandmaier , Andreas M.
Köhncke, Ylva
Ghisletta, Paolo
- DOI
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10.5964/qcmb.2979
- URN
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urn:nbn:de:101:1-2021072405134788428062
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:51 AM CEST
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Associated
- Cekic, Sezen
- Aichele, Stephen
- Brandmaier , Andreas M.
- Köhncke, Ylva
- Ghisletta, Paolo
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