|Title||Modelling recurrent events: a tutorial for analysis in epidemiology.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Amorim, Leila D. A. F., and Jianwen Cai|
|Journal||Int J Epidemiol|
|Date Published||2015 Feb|
|Keywords||Algorithms, Bias, Data Interpretation, Statistical, Epidemiologic Methods, Epidemiologic Studies, Humans, Models, Statistical|
In many biomedical studies, the event of interest can occur more than once in a participant. These events are termed recurrent events. However, the majority of analyses focus only on time to the first event, ignoring the subsequent events. Several statistical models have been proposed for analysing multiple events. In this paper we explore and illustrate several modelling techniques for analysis of recurrent time-to-event data, including conditional models for multivariate survival data (AG, PWP-TT and PWP-GT), marginal means/rates models, frailty and multi-state models. We also provide a tutorial for analysing such type of data, with three widely used statistical software programmes. Different approaches and software are illustrated using data from a bladder cancer project and from a study on lower respiratory tract infection in children in Brazil. Finally, we make recommendations for modelling strategy selection for analysis of recurrent event data.
|Alternate Journal||Int J Epidemiol|
|Original Publication||Modelling recurrent events: A tutorial for analysis in epidemiology.|
|PubMed Central ID||PMC4339761|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P01 CA 142538 / CA / NCI NIH HHS / United States
ULIRR025747 / / PHS HHS / United States
Modelling recurrent events: a tutorial for analysis in epidemiology.