Flexible parametric models for relative survival, with application in coronary heart disease
- 24 September 2007
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 26 (30), 5486-5498
- https://doi.org/10.1002/sim.3064
Abstract
Relative survival is frequently used in population‐based studies as a method for estimating disease‐related mortality without the need for information on cause of death. We propose an extension to relative survival of a flexible parametric model proposed by Royston and Parmar for censored survival data. The model provides smooth estimates of the relative survival and excess mortality rates by using restricted cubic splines on the log cumulative excess hazard scale. The approach has several advantages over some of the more standard relative survival models, which adopt a piecewise approach, the main being the ability to model time on a continuous scale, the survival and hazard functions are obtained analytically and it does not use split‐time data. Copyright © 2007 John Wiley & Sons, Ltd.Keywords
This publication has 23 references indexed in Scilit:
- Clinicians didn't reliably distinguish between different causes of cardiac death using case historiesJournal of Clinical Epidemiology, 2006
- The Effect on Treatment Comparisons of Different Measurement Frequencies in Human Immunodeficiency Virus Observational DatabasesAmerican Journal of Epidemiology, 2006
- Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time‐dependent effectsStatistics in Medicine, 2005
- Outcome of patients with newly diagnosed atrial fibrillation at the Mayo Clinic and residing in that areaThe American Journal of Cardiology, 2004
- An Individual Measure of Relative SurvivalJournal of the Royal Statistical Society Series C: Applied Statistics, 2004
- Regression models for relative survivalStatistics in Medicine, 2003
- Ten-Year Survival After First-Ever Stroke in the Perth Community Stroke StudyStroke, 2003
- Novel designs for multi‐arm clinical trials with survival outcomes with an application in ovarian cancerStatistics in Medicine, 2003
- Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effectsStatistics in Medicine, 2002
- Survival and cause of death after myocardial infarction: The Danish MONICA studyJournal of Clinical Epidemiology, 2001