On a general structure for hazard-based regression models: an application to population-based cancer research

TL;DR

This paper introduces a flexible hazard modeling framework that encompasses various hazard structures, including proportional and accelerated hazards, using the Exponentiated Weibull distribution, with demonstrated effectiveness in cancer epidemiology data analysis.

Contribution

It proposes a unified approach for hazard modeling using a flexible baseline distribution, enabling analysis of diverse hazard shapes and structures in survival data.

Findings

The model accurately captures different hazard shapes like bathtub and unimodal.

Good inferential properties demonstrated through simulation studies.

Effective hazard structure selection using AIC in practice.

Abstract

The proportional hazards model represents the most commonly assumed hazard structure when analysing time to event data using regression models. We study a general hazard structure which contains, as particular cases, proportional hazards, accelerated hazards, and accelerated failure time structures, as well as combinations of these. We propose an approach to apply these different hazard structures, based on a flexible parametric distribution (Exponentiated Weibull) for the baseline hazard. This distribution allows us to cover the basic hazard shapes of interest in practice: constant, bathtub, increasing, decreasing, and unimodal. In an extensive simulation study, we evaluate our approach in the context of excess hazard modelling, which is the main quantity of interest in descriptive cancer epidemiology. This study exhibits good inferential properties of the proposed model, as well as good performance when using the Akaike Information Criterion for selecting the hazard structure. An application on lung cancer data illustrates the usefulness of the proposed model.