Multidimensional penalized splines for survival models: illustration for net survival trend analyses

Author:

Dantony Emmanuelle1234ORCID,Uhry Zoé15ORCID,Fauvernier Mathieu1234,Coureau Gaëlle678,Mounier Morgane6910,Trétarre Brigitte61112ORCID,Molinié Florence61213ORCID,Roche Laurent1234,Remontet Laurent1234

Affiliation:

1. Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon , Lyon, France

2. Equipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558 , Villeurbanne, France

3. Université de Lyon , Lyon, France

4. Université Claude Bernard Lyon 1 , Villeurbanne, France

5. Direction des Maladies Non Transmissibles et des Traumatismes, Santé Publique France , Saint-Maurice, France

6. French Network of Cancer Registries (Francim) , Toulouse, France

7. Gironde General Cancer Registry, Univ Bordeaux , Bordeaux, France

8. Service d'information Médicale, CHU de Bordeaux , Bordeaux, France

9. Registre des Hémopathies Malignes de la Côte-d’Or, CHU de Dijon Bourgogne , Dijon, France

10. UMR INSERM 1231, Université Bourgogne Franche-Comté , Dijon, France

11. Hérault Cancer Registry , Montpellier, France

12. CERPOP, UMR 1295, Université de Toulouse III , Toulouse, France

13. Loire-Atlantique/Vendée Cancer Registry, SIRIC-ILIAD , Nantes, France

Abstract

Abstract Background In descriptive epidemiology, there are strong similarities between incidence and survival analyses. Because of the success of multidimensional penalized splines (MPSs) in incidence analysis, we propose in this pedagogical paper to show that MPSs are also very suitable for survival or net survival studies. Methods The use of MPSs is illustrated in cancer epidemiology in the context of survival trends studies that require specific statistical modelling. We focus on two examples (cervical and colon cancers) using survival data from the French cancer registries (cases 1990–2015). The dynamic of the excess mortality hazard according to time since diagnosis was modelled using an MPS of time since diagnosis, age at diagnosis and year of diagnosis. Multidimensional splines bring the flexibility necessary to capture any trend patterns while penalization ensures selecting only the complexities necessary to describe the data. Results For cervical cancer, the dynamic of the excess mortality hazard changed with the year of diagnosis in opposite ways according to age: this led to a net survival that improved in young women and worsened in older women. For colon cancer, regardless of age, excess mortality decreases with the year of diagnosis but this only concerns mortality at the start of follow-up. Conclusions MPSs make it possible to describe the dynamic of the mortality hazard and how this dynamic changes with the year of diagnosis, or more generally with any covariates of interest: this gives essential epidemiological insights for interpreting results. We use the R package survPen to do this type of analysis.

Funder

Institut National du Cancer

Publisher

Oxford University Press (OUP)

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1. Perspective Chapter: Enhancing Regression Analysis with Splines and Machine Learning – Evaluation of How to Capture Complex Non-Linear Multidimensional Variables;Nonlinear Systems and Matrix Analysis - Recent Advances in theory and Applications [Working Title];2024-09-11

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