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

TY - JOUR

T1 - On a general structure for hazard-based regression models:

T2 - An application to population-based cancer research

AU - Rubio Alvarez, Francisco Javier

AU - Remontet, Laurent

AU - Jewell, Nicholas P

AU - Belot, Aurelien

PY - 2018/8/1

Y1 - 2018/8/1

N2 - The proportional hazards model represents the most commonly assumed hazard structure when analysing time to eventdata 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 toapply 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.

AB - The proportional hazards model represents the most commonly assumed hazard structure when analysing time to eventdata 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 toapply 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.

U2 - 10.1177/0962280218782293

DO - 10.1177/0962280218782293

M3 - Article

SN - 0962-2802

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

ER -