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

Tanya P. Garcia, Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)552-565
Number of pages14
JournalLifetime Data Analysis
Volume17
Issue number4
DOIs
StatePublished - Oct 1 2011

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Survival Model
Log-rank Test
Covariates
Biometrics
Non-proportional Hazards
Randomized Clinical Trial
Estimating Equation
Survival Data
Survival Time
Leukemia
Hazards
Simulation Study
Model
Estimator
Class

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Cite this

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Efficiency improvement in a class of survival models through model-free covariate incorporation. / Garcia, Tanya P.; Ma, Yanyuan; Yin, Guosheng.

In: Lifetime Data Analysis, Vol. 17, No. 4, 01.10.2011, p. 552-565.

Research output: Contribution to journalArticle

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