Likelihood approaches to the non-parametric two-sample problem for right-censored data.
The classical two-sample problem with random right-censoring is considered. We show that non- parametric likelihood techniques can be used to obtain tests for either the identity hypothesis or the non-parametric Behrens-Fisher hypothesis (NBFH). In the case of the identity hypothesis, a special imputed permutation distribution is used to estimate the distribution under the null hypothesis. In the case of the NBFH, simulation from the constrained non-parametric maximum likelihood estimate is used. Simulation shows that the tests using either approximation have excellent control of the type I error rate, even with quite small sample sizes. Further, for Lehmann-type alternatives the likelihood-based methods have similar power to the logrank test, while for the non-Lehmann-type alternatives tried here the likelihood-based methods have superior power.
Troendle
James F
JF
Biometry and Mathematical Statistics Branch, Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, National Institutes of Health, DHHS, Bethesda, MD 20892, USA. jt3t@nih.gov
Yu
Kai F
KF
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007743:Labor, Obstetric / Q000502:physiology; D016013:Likelihood Functions; D011247:Pregnancy; D018709:Statistics, Nonparametric