A relative survival regression model using B‐spline functions to model non‐proportional hazards
- 19 August 2003
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 22 (17), 2767-2784
- https://doi.org/10.1002/sim.1484
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.Keywords
This publication has 22 references indexed in Scilit:
- Time-Dependent Hazard Ratio: Modeling and Hypothesis Testing with Application in Lupus NephritisJournal of the American Statistical Association, 1996
- Proportional excess hazardsBiometrika, 1996
- Hazard RegressionJournal of the American Statistical Association, 1995
- Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer PrognosisJournal of the American Statistical Association, 1992
- Nonparametric Survival Analysis with Time-Dependent Covariate Effects: A Penalized Partial Likelihood ApproachThe Annals of Statistics, 1990
- Flexible regression models with cubic splinesStatistics in Medicine, 1989
- Monotone Regression Splines in ActionStatistical Science, 1988
- Regression Analysis of Relative Survival RatesJournal of the Royal Statistical Society Series C: Applied Statistics, 1987