Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Stat Med. 2015 Jun 30;34(14):2181-95. doi: 10.1002/sim.6141. Epub 2014 Mar 14.


Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.

Keywords: applications; random effects; software; time-dependent.

Publication types

  • Review

MeSH terms

  • Anti-HIV Agents / pharmacology
  • Bayes Theorem
  • Biomarkers, Pharmacological / blood
  • CD4 Lymphocyte Count
  • Clinical Trials as Topic / methods*
  • Clinical Trials as Topic / statistics & numerical data
  • Drug Design
  • Epidemiologic Research Design*
  • Graft Rejection / immunology
  • Graft Rejection / prevention & control
  • HIV Infections / drug therapy
  • HIV Infections / immunology
  • HIV Infections / virology
  • Humans
  • Kidney Transplantation / adverse effects
  • Longitudinal Studies
  • Models, Statistical*
  • Proportional Hazards Models
  • Quality of Life
  • Renal Insufficiency, Chronic / surgery
  • Software
  • Survival Analysis*
  • Viral Load


  • Anti-HIV Agents
  • Biomarkers, Pharmacological