Preprint

Smooth hazards with multiple time scales

Carollo, A., Eilers, P. H. C., Putter, H., Gampe, J.
arXiv e-prints 2305.09342
27 pages.
arXiv
submitted on: 16 May 2023 (version 1) (2023), unpublished
Open Access
Reproducible

Abstract

Hazard models are the most commonly used tool to analyse time-to-event data. If more than one time scale is relevant for the event under study, models are required that can incorporate the dependence of a hazard along two (or more) time scales. Such models should be flexible to capture the joint influence of several times scales and nonparametric smoothing techniques are obvious candidates. P-splines offer a flexible way to specify such hazard surfaces, and estimation is achieved by maximizing a penalized Poisson likelihood. Standard observations schemes, such as right-censoring and left-truncation, can be accommodated in a straightforward manner. The model can be extended to proportional hazards regression with a baseline hazard varying over two scales. Generalized linear array model (GLAM) algorithms allow efficient computations, which are implemented in a companion R-package.

Keywords: event history analysis, proportional hazard models, smoothing, statistical analysis
The Max Planck Institute for Demographic Research (MPIDR) in Rostock is one of the leading demographic research centers in the world. It's part of the Max Planck Society, the internationally renowned German research society.