Robust covariate-adjusted logrank tests

• 成果类型: Article
• 署名作者: Kong, FH; Slud, E
• 署名单位: Westat; University System of Maryland; University of Maryland College Park
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/84.4.847
• 发表日期: 1997
• 页码: 847862
• 关键词: PROPORTIONAL HAZARDS MODEL; regression-models; survival-data; cox model; EFFICIENCY

摘要

In testing for significance of treatment effect within two-sample censored survival data with measured covariates, it is known (Tsiatis, Rosner & Tritchler, 1985) that adjusting the logrank test statistic using a proportional-hazards model for covariate effect can substantially increase efficiency. Extending the robust score statistics described by Lin & Wei (1989), we show how to estimate and optimise the relative efficiencies of such score statistics based on various possible working models, providing a flexible and statistically valid two-sample testing methodology which improves upon the purely nonparametric logrank without sacrificing model robustness. Numerical calculations indicate some nonproportional-hazard settings within which the power of the logrank is erratic while the tests described here have reliably good power.