In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques.
Keywords: dynamic prediction; epidemiology; informative censoring; joint models; methodology.
© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA.