Semiparametric regression for discrete time-to-event data. (June 2018)

• Record Type: Journal Article
• Authors: Berger, Moritz; Schmid, Matthias
• Other Names: Groll Andreas guest-editor.; Kneib Thomas guest-editor.; Mayr Andreas guest-editor.
• Is Part Of: Statistical modelling. Volume 18:Number 3/4(2018)
• Page Start: 322
• Page End: 345
• Publication Date: 2018-06
• Subjects: Discrete time-to-event data -- Hazard models -- semiparametric regression -- survival analysis; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X17748084
• Languages: English
• ISSNs: 1471-082X

Abstract

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.