Lifetime Data Anal - Efficiency improvement in a class of survival models through model-free covariate incorporation.

Tópicos

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Resumo

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.

Resumo Limpo

random clinic trial often concern compar twosampl surviv data although logrank test usual suitabl purpos may result substanti power loss two group nonproport hazard general class surviv model yang prentic biometrika includ logrank test special case improv model effici incorpor auxiliari covari correl surviv time modelfre form augment estim equat auxiliari covari establish effici improv use semiparametr theori zhang et al biometr lu tsiati biometr minim assumpt approach produc unbias asymptot normal estim addit effici gain simul studi applic leukemia studi show satisfactori perform propos method

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