Research Article

A relative survival regression model using B-spline functions to model non-proportional hazards

  1. Roch Giorgi1,3,
  2. Michal Abrahamowicz2,
  3. Catherine Quantin3,*,
  4. Philippe Bolard4,
  5. Jacques Esteve5,
  6. Joanny Gouvernet1,
  7. Jean Faivre4

Article first published online: 19 AUG 2003

DOI: 10.1002/sim.1484

Issue

Statistics in Medicine

Statistics in Medicine

Volume 22, Issue 17, pages 2767–2784, 15 September 2003

Additional Information

How to Cite

Giorgi, R., Abrahamowicz, M., Quantin, C., Bolard, P., Esteve, J., Gouvernet, J. and Faivre, J. (2003), A relative survival regression model using B-spline functions to model non-proportional hazards. Statistics in Medicine, 22: 2767–2784. doi: 10.1002/sim.1484

Author Information

  1. 1

    LERTIM, Faculté de Médecine, Université de la Méditerranée, 27 Bd Jean Moulin 13385 Marseille Cedex, France

  2. 2

    Department of Epidemiology and Biostatistics, McGill University, Division of Clinical Epidemiology, Montreal General Hospital, 1650 Cedar Avenue, Montreal, Quebec, H3G 1A4 Canada

  3. 3

    Department of Biostatistics, Centre Hospitalier Universitaire (University teaching hospital) de Dijon, BP 1542, 21034 Dijon Cedex, France

  4. 4

    Registre Bourguignon des Tumeurs Digestives, Faculté de Médecine, 7 Bd Jeanne d'Arc 21033 Dijon Cedex, France

  5. 5

    Department of Biostatistics, Centre Hospitalier Universitaire de Lyon, 69495 Pierre Bénite, France

*Service de Biostatistique et Informatique Médicale, Centre Hospitalier Universitaire de Dijon, 1 Boulevard Jeanne d'Arc, BP 1542, 21034 Dijon Cedex, France

Publication History

  1. Issue published online: 19 AUG 2003
  2. Article first published online: 19 AUG 2003
  3. Manuscript Accepted:
  4. Manuscript Received:

Funded by

  • French Association for Research against Cancer (ACR)
  • French Ligue Bourguignonne Contre le Cancer
  • Burgundy Regional Council

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Keywords:

  • relative survival;
  • non-proportional hazards;
  • B-splines;
  • colon cancer;
  • survival analysis

Abstract

Relative survival, a method for assessing prognostic factors for disease-specific mortality in unselected populations, is frequently used in population-based studies. However, most relative survival models assume that the effects of covariates on disease-specific mortality conform with the proportional hazards hypothesis, which may not hold in some long-term studies. To accommodate variation over time of a predictor's effect on disease-specific mortality, we developed a new relative survival regression model using B-splines to model the hazard ratio as a flexible function of time, without having to specify a particular functional form. Our method also allows for testing the hypotheses of hazards proportionality and no association on disease-specific hazard. Accuracy of estimation and inference were evaluated in simulations. The method is illustrated by an analysis of a population-based study of colon cancer. Copyright © 2003 John Wiley & Sons, Ltd.

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