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

Research output: Contribution to journalArticle

Francisco Javier Rubio Alvarez, Laurent Remontet, Nicholas P Jewell, Aurelien Belot

Original languageEnglish
Number of pages14
JournalStatistical Methods in Medical Research
Early online date1 Aug 2018
Publication statusE-pub ahead of print - 1 Aug 2018


King's Authors


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.

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