Covariate-adjusted log-rank test: guaranteed efficiency gain and universal applicability
针对临床试验中右删失时间事件数据，提出一种协变量调整的对数秩检验，公式简单，能保证比未调整检验效率更高，且适用于简单随机化和多种协变量自适应随机化方案。
Summary Nonparametric covariate adjustment is considered for log-rank-type tests of the treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization and all commonly used covariate-adaptive randomization schemes such as the stratified permuted block and the Pocock–Simon minimization, which is not a property enjoyed by the unadjusted log-rank test. Our method is supported by novel asymptotic theory and empirical results for Type-I error and power of tests.