{
  "creator": [
    "Cekic, Sezen",
    "Aichele, Stephen ",
    "Brandmaier , Andreas M. ",
    "Köhncke, Ylva ",
    "Ghisletta, Paolo "
  ],
  "date": [
    "2021-05-11"
  ],
  "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."
  ],
  "format": [
    "application/pdf",
    "text/html",
    "text/xml"
  ],
  "identifier": [
    "https://qcmb.psychopen.eu/index.php/qcmb/article/view/2979",
    "10.5964/qcmb.2979"
  ],
  "language": [
    "eng"
  ],
  "publisher": [
    "PsychOpen GOLD / Leibniz Institut for Psychology (ZPID)"
  ],
  "relation": [
    "https://qcmb.psychopen.eu/index.php/qcmb/article/view/2979/2979.pdf",
    "https://qcmb.psychopen.eu/index.php/qcmb/article/view/2979/2979.html",
    "https://qcmb.psychopen.eu/index.php/qcmb/article/view/2979/2979.xml"
  ],
  "rights": [
    "Copyright (c) 2021 Sezen Cekic, Stephen  Aichele, Andreas M.  Brandmaier , Ylva  Köhncke, Paolo  Ghisletta",
    "https://creativecommons.org/licenses/by/4.0"
  ],
  "source": [
    "Quantitative and Computational Methods in Behavioral Sciences; 2021; 1-40",
    "2699-8432",
    "10.5964/qcmb.v1i1"
  ],
  "subject": [
    "tutorial",
    "joint model",
    "mixed-effects model",
    "time-to-event",
    "association structures",
    "JMbayes package",
    "application"
  ],
  "title": [
    "A Tutorial for Joint Modeling of Longitudinal and Time-to-Event Data in R"
  ],
  "type": [
    "info:eu-repo/semantics/article",
    "info:eu-repo/semantics/publishedVersion"
  ]
}