Smooth hazards with multiple time scales

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submitted on: 16 May 2023 (version 1) (2023), unpublished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


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.

Schlagwörter: event history analysis, proportional hazard models, smoothing, statistical analysis
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