Statistical Modelling 18 (3-4) (2018), 322–345
Semiparametric regression for discrete time-to-event data
Moritz Berger
Department of Medical Biometry,
Informatics and Epidemiology,
University Hospital Bonn
Germany
e-mail: Moritz.Berger@imbie.uni-bonn.de
Matthias Schmid
Department of Medical Biometry,
Informatics and Epidemiology,
University Hospital Bonn
Germany
Abstract:
Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here, we focus on the analysis of time-to-event outcomes that are either intrinsically discrete or grouped versions of continuous event times. In the literature, there exists a variety of regression methods for such data. This tutorial provides an introduction to how these models can be applied using open source statistical software. In particular, we consider semiparametric extensions comprising the use of smooth nonlinear functions and tree-based methods. All methods are illustrated by data on the duration of unemployment of US citizens.
Keywords:
Discrete time-to-event data; Hazard models; semiparametric regression; survival analysis.
Downloads:
Example data and code in
zipped archive.
back