Rubio, FJ;
Remontet, L;
Jewell, NP;
Belot, A;
(2019)
On a general structure for hazard-based regression models: An application to population-based cancer research.
Statistical Methods in Medical Research
, 28
(8)
pp. 2404-2417.
10.1177/0962280218782293.
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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.
| Type: | Article |
|---|---|
| Title: | On a general structure for hazard-based regression models: An application to population-based cancer research |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/0962280218782293 |
| Publisher version: | http://dx.doi.org/10.1177/0962280218782293 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | General hazard structure, accelerated failure time, accelerated hazards, excess hazard, exponentiated Weibull distribution, net survival, proportional hazards, Aged, Aged, 80 and over, Comorbidity, Computer Simulation, England, Female, Humans, Lung Neoplasms, Proportional Hazards Models, Registries, Survival Analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10126575 |
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