Piecewise Constant Hazard Estimation with the Fused Lasso

TL;DR

This paper introduces a data-driven method for estimating piecewise constant hazard functions in time-to-event analysis, using fused lasso techniques within a flexible counting process framework, applicable to various models including Cox and multi-state models.

Contribution

It develops a novel, fully data-driven fused lasso approach for estimating change points and hazard levels in complex time-to-event models, with theoretical guarantees.

Findings

Estimator converges at a quantifiable rate.

Change points are accurately approximated.

Method performs well in simulations and real data.

Abstract

In applied time-to-event analysis, a flexible parametric approach is to model the hazard rate as a piecewise constant function of time. However, the change points and values of the piecewise constant hazard are usually unknown and need to be estimated. In this paper, we develop a fully data-driven procedure for piecewise constant hazard estimation. We work in a general counting process framework which nests a wide range of popular models in time-to-event analysis including Cox's proportional hazards model with potentially high-dimensional covariates, competing risks models as well as more general multi-state models. To construct our estimator, we set up a regression model for the increments of the Breslow estimator and then use fused lasso techniques to approximate the piecewise constant signal in this regression model. In the theoretical part of the paper, we derive the convergence rate of our estimator as well as some results on how well the change points of the piecewise constant hazard are approximated by our method. We complement the theory by both simulations and a real data example, illustrating that our results apply in rather general event histories such as multi-state models.