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

Backgound: The term ‘joint modelling ’ is used in the statistical literature to refer to meth-ods 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. Methods: We describe the development of the joint modelling framework and compare the results with those obtained by the more widely used approaches of conducting sep-arate analyses of the repeated measurements and survival times based on a linear mixed effects model and a Cox model, respectively. Our case study concerns a data set from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS). We also pro