Nonparametric Tests for Continuous Covariate Effects with Multistate Survival Data
Article first published online: 11 FEB 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1080–1089, December 2008
How to Cite
Peng, L. and Fine, J. P. (2008), Nonparametric Tests for Continuous Covariate Effects with Multistate Survival Data. Biometrics, 64: 1080–1089. doi: 10.1111/j.1541-0420.2008.00985.x
- Issue published online: 24 NOV 2008
- Article first published online: 11 FEB 2008
- Received May 2007. Revised October 2007. Accepted November 2007.
- Empirical process theory;
- Integral tests;
- Multiple event types;
- Nonparametric estimation;
- Uniform representations
Summary In clinical trials and observational studies, it is often of scientific interest to evaluate the effects of covariates on complex multistate event probabilities. With discrete covariates, nonparametric tests may be constructed using estimates of the relevant quantities. With continuous covariates, a common approach is to arbitrarily discretize the covariates, which may lead to substantial information loss. Another strategy is to formulate the covariate effects in a regression model. Model-based tests may have either low power or be biased under misspecification. We propose nonparametric tests not requiring arbitrary discretization. The tests involve integrals of estimates continuously indexed by dichotomizations of the covariates. General asymptotic results are derived under null and alternative hypotheses, and verified using empirical process theory in several special cases. The tests are consistent under stochastic ordering, which arises naturally with multistate data. A novel nonparametric measure of covariate effect is studied as a natural byproduct of the testing procedure. Simulation studies and two real data analyses demonstrate the gains of the new testing procedure over those based either on categorization or on regression models.