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比例子分布风险模型的拟合优度检验。

Goodness-of-fit test for proportional subdistribution hazards model.

机构信息

Division of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, U.S.A.; VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, U.S.A.

出版信息

Stat Med. 2013 Sep 30;32(22):3804-11. doi: 10.1002/sim.5815. Epub 2013 Apr 28.

Abstract

This paper concerns using modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine-Gray model, similar to the tests proposed by Grambsch and Therneau for independently censored data. We develop a score test for the time-varying coefficients based on the modified Schoenfeld residuals derived assuming a certain form of non-proportionality. The methods perform well in simulations and a real data analysis of breast cancer data, where the treatment effect exhibits non-proportional hazards.

摘要

本文探讨了使用修正加权 Schoenfeld 残差检验 Fine-Gray 模型的亚分布风险比例性,类似于 Grambsch 和 Therneau 针对独立删失数据提出的检验方法。我们基于假设某种非比例性形式的修正 Schoenfeld 残差,开发了一种针对时变系数的得分检验方法。这些方法在模拟和乳腺癌数据的实际分析中表现良好,其中治疗效果表现出非比例风险。

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