Efficiency improvement in a class of survival models through model-free covariate incorporation

Tanya P. Garcia, Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)552-565
Number of pages14
JournalLifetime Data Analysis
Issue number4
StatePublished - Oct 2011

All Science Journal Classification (ASJC) codes

  • Applied Mathematics


Dive into the research topics of 'Efficiency improvement in a class of survival models through model-free covariate incorporation'. Together they form a unique fingerprint.

Cite this