Premium
A nonparametric test to compare survival distributions with covariate adjustment
Author(s) -
Heller Glenn,
Venkatraman E. S.
Publication year - 2004
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2004.b5364.x
Subject(s) - covariate , statistics , test statistic , mathematics , proportional hazards model , nonparametric statistics , survival function , statistic , analysis of covariance , econometrics , statistical hypothesis testing , survival analysis
Summary. The analysis of covariance is a technique that is used to improve the power of a k ‐sample test by adjusting for concomitant variables. If the end point is the time of survival, and some observations are right censored, the score statistic from the Cox proportional hazards model is the method that is most commonly used to test the equality of conditional hazard functions. In many situations, however, the proportional hazards model assumptions are not satisfied. Specifically, the relative risk function is not time invariant or represented as a log‐linear function of the covariates. We propose an asymptotically valid k ‐sample test statistic to compare conditional hazard functions which does not require the assumption of proportional hazards, a parametric specification of the relative risk function or randomization of group assignment. Simulation results indicate that the performance of this statistic is satisfactory. The methodology is demonstrated on a data set in prostate cancer.