Covariate adjustment for non-parametric tests for censored survival data.
We develop a method for covariate adjustment for a general class of non-parametric tests for censored survival data which includes the widely used logrank and Wilcoxon tests. The covariate-adjusted tests improve the power of the unadjusted counterparts and have advantages over the covariate-adjusted Cox score test when there are outliers in the covariable space. We investigate the small sample properties of such test statistics through Monte Carlo simulations. Examples are given to illustrate the proposed procedures.
Li
Z
Z
Department of Biometrics and Statistical Sciences, The Procter & Gamble Company, 8700 Mason-Montgomery Rd., Box 2199, Mason, OH 45040, USA. li.z@pg.com
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001806:Blood Urea Nitrogen; D003198:Computer Simulation; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000401:mortality; D007958:Leukocyte Count; D008875:Middle Aged; D009010:Monte Carlo Method; D009101:Multiple Myeloma / Q000401:mortality; D016016:Proportional Hazards Models; D018709:Statistics, Nonparametric; D016019:Survival Analysis