Joint modelling of repeated measurement and time-to-event data: an introductory tutorial


Asar Ö. , Ritchie J., Kalra P. A. , Diggle P. J.

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, vol.44, no.1, pp.334-344, 2015 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 1
  • Publication Date: 2015
  • Doi Number: 10.1093/ije/dyu262
  • Title of Journal : INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
  • Page Numbers: pp.334-344

Abstract

Backgound: The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (eGFR) measurements and time to initiation of renal replacement therapy (RRT). Joint models typically combine linear mixed effects models for repeated measurements and Cox models for censored survival outcomes. Our aim in this paper is to present an introductory tutorial on joint modelling methods, with a case study in nephrology.