Graphical exploration of covariate effects on survival data through nonparametric quantile curves.
Kaplan-Meier curves provide an effective means of presenting the distributional pattern in a sample of survival data. However, in order to assess the effect of a covariate, a standard scatterplot is often difficult to interpret because of the presence of censored observations. Several authors have proposed a running median as an effective way of indicating the effect of a covariate. This article proposes a form of kernel estimation, employing double smoothing, that can be applied in a simple and efficient manner to construct an estimator of a percentile of the survival distribution as a function of one or two covariates. Permutations and bootstrap samples can be used to construct reference bands that help identify whether particular features of the estimates indicate real features of the underlying curve or whether this may be due simply to random variation. The techniques are illustrated on data from a study of kidney transplant patients.
Bowman
A W
AW
Department of Statistics, University of Glasgow, UK. adrian@stats.gla.ac.uk
Wright
E M
EM
D001699:Biometry / Q000379:methods*; D003404:Creatinine / Q000097:blood; D006801:Humans; D016030:Kidney Transplantation / Q000401:mortality / Q000502:physiology; D015233:Models, Statistical; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D015996:Survival Rate; D013997:Time Factors
D003404:Creatinine