title: Efficiency improvement in a class of survival models through model-free covariate incorporation.
abstract: In randomized clinical trials, we are often concerned with comparing two-sample survival data. Although the log-rank test is usually suitable for this purpose, it may result in substantial power loss when the two groups have nonproportional hazards. In a more general class of survival models of Yang and Prentice (Biometrika 92:1-17, 2005), which includes the log-rank test as a special case, we improve model efficiency by incorporating auxiliary covariates that are correlated with the survival times. In a model-free form, we augment the estimating equation with auxiliary covariates, and establish the efficiency improvement using the semiparametric theories in Zhang et al. (Biometrics 64:707-715, 2008) and Lu and Tsiatis (Biometrics, 95:674-679, 2008). Under minimal assumptions, our approach produces an unbiased, asymptotically normal estimator with additional efficiency gain. Simulation studies and an application to a leukemia study show the satisfactory performance of the proposed method.
authors: Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA. tpgarcia@stat.tamu.edu
authors: Tanya P
authors: TP
authors: Garcia
authors: Yanyuan
authors: Y
authors: Ma
authors: Guosheng
authors: G
authors: Yin
mesh_terms: D000328:Adult; D000368:Aged; D058846:Antibodies, Monoclonal, Murine-Derived / Q000627:therapeutic use; D000970:Antineoplastic Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008228:Lymphoma, Non-Hodgkin / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D000069283:Rituximab
chemical_list: D058846:Antibodies, Monoclonal, Murine-Derived; D000970:Antineoplastic Agents; D000069283:Rituximab