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

Survival model cancer hazard net survival penalized splines trend analyses

Journal

International journal of epidemiology
ISSN: 1464-3685
Titre abrégé: Int J Epidemiol
Pays: England
ID NLM: 7802871

Informations de publication

Date de publication:
14 Feb 2024
Historique:
received: 08 06 2023
accepted: 13 02 2024
medline: 19 3 2024
pubmed: 19 3 2024
entrez: 18 3 2024
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 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.

Identifiants

pubmed: 38499394
pii: 7631330
doi: 10.1093/ije/dyae033
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Institut National du Cancer
ID : 19DMNA021-0

Informations de copyright

© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Auteurs

Emmanuelle Dantony (E)

Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Equipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France.
Université de Lyon, Lyon, France.
Université Claude Bernard Lyon 1, Villeurbanne, France.

Zoé Uhry (Z)

Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Direction des Maladies Non Transmissibles et des Traumatismes, Santé Publique France, Saint-Maurice, France.

Mathieu Fauvernier (M)

Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Equipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France.
Université de Lyon, Lyon, France.
Université Claude Bernard Lyon 1, Villeurbanne, France.

Gaëlle Coureau (G)

French Network of Cancer Registries (Francim), Toulouse, France.
Gironde General Cancer Registry, Univ Bordeaux, Bordeaux, France.
Service d'information Médicale, CHU de Bordeaux, Bordeaux, France.

Morgane Mounier (M)

French Network of Cancer Registries (Francim), Toulouse, France.
Registre des Hémopathies Malignes de la Côte-d'Or, CHU de Dijon Bourgogne, Dijon, France.
UMR INSERM 1231, Université Bourgogne Franche-Comté, Dijon, France.

Brigitte Trétarre (B)

French Network of Cancer Registries (Francim), Toulouse, France.
Hérault Cancer Registry, Montpellier, France.
CERPOP, UMR 1295, Université de Toulouse III, Toulouse, France.

Florence Molinié (F)

French Network of Cancer Registries (Francim), Toulouse, France.
CERPOP, UMR 1295, Université de Toulouse III, Toulouse, France.
Loire-Atlantique/Vendée Cancer Registry, SIRIC-ILIAD, Nantes, France.

Laurent Roche (L)

Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Equipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France.
Université de Lyon, Lyon, France.
Université Claude Bernard Lyon 1, Villeurbanne, France.

Laurent Remontet (L)

Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Equipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, CNRS UMR 5558, Villeurbanne, France.
Université de Lyon, Lyon, France.
Université Claude Bernard Lyon 1, Villeurbanne, France.

Classifications MeSH