A generalized additive model approach to time-to-event analysis. (June 2018)

• Record Type: Journal Article
• Authors: Bender, Andreas; Groll, Andreas; Scheipl, Fabian
• 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: 299
• Page End: 321
• Publication Date: 2018-06
• Subjects: Cox model -- Time-varying coefficients -- piece-wise exponential model -- penalization -- survival analysis -- splines; 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: 10.1177/1471082X17748083
• Languages: English
• ISSNs: 1471-082X

Abstract

Abstract: This tutorial article demonstrates how time-to-event data can be modelled in a very flexible way by taking advantage of advanced inference methods that have recently been developed for generalized additive mixed models. In particular, we describe the necessary pre-processing steps for transforming such data into a suitable format and show how a variety of effects, including a smooth nonlinear baseline hazard, and potentially nonlinear and nonlinearly time-varying effects, can be estimated and interpreted. We also present useful graphical tools for model evaluation and interpretation of the estimated effects. Throughout, we demonstrate this approach using various application examples. The article is accompanied by a newR -package calledpammtools implementing all of the tools described here.