Clinically relevant graphical predictions from Bayesian joint longitudinal-survival models

Laura A. Hatfield, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Recent interest in understanding the effect of interventions on patient-reported outcomes as well as traditional clinical endpoints has led to an expansion of methods for simultaneous modeling of longitudinal and survival data in clinical trials. Such joint models link the multiple outcome measures using an underlying latent structure, typically a collection of individual-level random effects. They can estimate treatment effects separately on different aspects of a disease process, as well as illuminate associations among outcomes and individual variability. In communicating model output to clinicians and patients, it is challenging to convey a meaningful interpretation of multiple treatment effects, complex outcome associations, and important underlying assumptions. This paper presents graphical displays designed to make the output of Bayesian joint models accessible to non-technical audiences, while preserving important methodological features. We emphasize individual-level posterior predictions of longitudinal and survival outcomes, illustrating our methods using patient-reported symptom severity and survival in a clinical trial example.

Original languageEnglish (US)
Pages (from-to)169-181
Number of pages13
JournalHealth Services and Outcomes Research Methodology
Issue number2-3
StatePublished - Jun 2012


  • Hierarchical Bayesian methods
  • Joint longitudinal-survival models
  • Markov chain Monte Carlo
  • Parameter interpretation
  • Patient-reported outcomes
  • Statistical graphics


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