Framework and optimisation procedure for flexible parametric survival models

Affiliations

¹ Service de Biostatistique Hospices Civils de Lyon Lyon France; Laboratoire de Biostatistique-Santé UMR 5558 CNRS Paris France and, Université Claude Bernard Lyon 1, Lyon, France.
² Cancer Research UK, Cancer Survival Group, Faculty of Epidemiology and Population Health, Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

Comment

Laurent Remontet et al. Stat Med. 2015.

. 2015 Nov 10;34(25):3376-7.

doi: 10.1002/sim.6489.

¹ Service de Biostatistique Hospices Civils de Lyon Lyon France; Laboratoire de Biostatistique-Santé UMR 5558 CNRS Paris France and, Université Claude Bernard Lyon 1, Lyon, France.
² Cancer Research UK, Cancer Survival Group, Faculty of Epidemiology and Population Health, Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

No abstract available

Comment in

Reply to Letter to the Editor by Remontet et al.
Crowther MJ, Lambert PC. Crowther MJ, et al. Stat Med. 2015 Nov 10;34(25):3378-80. doi: 10.1002/sim.6606. Stat Med. 2015. PMID: 26434359 No abstract available.

An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.
Remontet L, Bossard N, Belot A, Estève J; French network of cancer registries FRANCIM. Remontet L, et al. Stat Med. 2007 May 10;26(10):2214-28. doi: 10.1002/sim.2656. Stat Med. 2007. PMID: 16900570
A general framework for parametric survival analysis.
Crowther MJ, Lambert PC. Crowther MJ, et al. Stat Med. 2014 Dec 30;33(30):5280-97. doi: 10.1002/sim.6300. Epub 2014 Sep 15. Stat Med. 2014. PMID: 25220693