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A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology

Rubio Alvarez, Francisco Javier; Eletti, Alessia; Marra, Giampiero; Quaresma, Manuela; Radice, Rosalba; (2022) A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology. Journal of the Royal Statistical Society Series C: Applied Statistics , 71 (4) pp. 1044-1062. 10.1111/rssc.12566. Green open access

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Abstract

Excess hazard modelling is one of the main tools in population-based cancer survival research. Indeed, this setting allows for direct modelling of the survival due to cancer even in the absence of reliable information on the cause of death, which is common in population-based cancer epidemiology studies. We propose a unifying link-based additive modelling framework for the excess hazard that allows for the inclusion of many types of covariate effects, including spatial and time-dependent effects, using any type of smoother, such as thin plate, cubic splines, tensor products and Markov random fields. In addition, this framework accounts for all types of censoring as well as left truncation. Estimation is conducted by using an efficient and stable penalized likelihood-based algorithm whose empirical performance is evaluated through extensive simulation studies. Some theoretical and asymptotic results are discussed. Two case studies are presented using population-based cancer data from patients diagnosed with breast (female), colon and lung cancers in England. The results support the presence of non-linear and time-dependent effects as well as spatial variation. The proposed approach is available in the R package GJRM.

Type: Article
Title: A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12566
Publisher version: https://doi.org/10.1111/rssc.12566
Language: English
Additional information: © 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons, Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: additive predictor, excess hazard, left truncation, link function, mixed censoring, net survival, penalized log-likelihood, regression splines, spatial effects, survival data
UCL classification: 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
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10147419
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