A non parametric test to compare survival distributions with covariate adjustment Journal Article
| Authors: | Heller, G.; Venkatraman, E. S. |
| Article Title: | A non parametric test to compare survival distributions with covariate adjustment |
| Abstract: | 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. |
| Keywords: | survival data; covariate adjusted k-sample test; kernel smoothing; nonparametric test statistic; u-statistic |
| Journal Title: | Journal of the Royal Statistical Society Series B - Statistical Methodology |
| Volume: | 66 |
| Issue: | 3 |
| ISSN: | 1369-7412 |
| Publisher: | Wiley Blackwell |
| Date Published: | 2004-01-01 |
| Start Page: | 719 |
| End Page: | 733 |
| Language: | English |
| DOI: | 10.1111/j.1467-9868.2004.b5364.x |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | J. R. Stat. Soc. Ser. B Stat. Methodol. -- Cited By (since 1996):4 -- Export Date: 16 June 2014 -- Source: Scopus |
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