基于两条生存曲线间面积的非参数统计推断方法研究

Nonparametric Statistical Inference Methods based on the Area between Two Survival Curves

目的在生存数据组间比较研究中,当风险率成比例假设失效,特别是生存曲线交叉时,Log-rank检验的检验效能很低,本文介绍和研究一类无上述假设条件的检验法。方法首先介绍一种基于两条生存曲线间面积值的检验法,其次基于置换检验思想提出校正的置换面积检验法,并通过Monte Carlo模拟将上述两种方法与常用的Log-rank和加权Kaplan-Meier检验进行性能比较和评价。结果模拟结果显示,在I类错误上除面积检验法偏离较大外,其余检验法仅有轻微波动。在检验效能方面,风险率成比例假设满足时,Log-rank的检验效能最高;生存曲线交叉于早期时,面积检验和置换面积检验的检验效能最高;除此之外,置换面积检验法效能最高。结论当生存数据风险率成比例假设成立时,推荐Log-rank检验;但当该假设失效,特别是生存曲线出现交叉时,推荐使用置换面积检验法。

Objective In the comparative study of two groups for time-to-event data,when the proportional hazards assumption is violated,especially two survival curves cross,the power of Log-rank test is low to lose reliability,this paper introduced and proposed a kind of methods without proportional hazards assumption.Methods First,we introduced a method based on the area between survival curves(i.e.the area test).Second,a permutation test to adjust the area value test(i.e.the permutation area test)was proposed.Last,the performance of Log-rank test、weighted Kaplan-Meier test,the area test and the proposed permutation area test were compared by Monte Carlo simulation.Results The simulations showed that the type I error of the permutation area test and other methods were slightly fluctuated while the area test deviated from significant level.Log-rank test had the highest power under the assumption of proportional hazards.The area test and the permutation area test were better than other methods when survival curves crossed early;in addition,the permutation area test outperformed other methods.Conclusion Log-rank test is recommended under the assumption of proportional hazards;the permutation area test is robust and recommended when the proportional hazard assumption is violated,especially when two survival curves cross.