A nonparametric test to compare survival distributions with covariate adjustment

• 成果类型: Article
• 署名作者: Heller, G; Venkatraman, ES
• 署名单位: Memorial Sloan Kettering Cancer Center
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2004.b5364.x
• 发表日期: 2004
• 页码: 719-733
• 关键词: models

摘要

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.