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一类用于分层生存数据的置换检验。

A class of permutation tests for stratified survival data.

作者信息

Shih J H

机构信息

Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892-7938, USA.

出版信息

Biometrics. 1999 Dec;55(4):1156-61. doi: 10.1111/j.0006-341x.1999.01156.x.

Abstract

We propose a class of permutation tests for stratified survival data. The tests are derived using the framework of Fay and Shih (1998, Journal of the American Statistical Association 93, 387-396), which creates tests by permuting scores based on a functional of estimated distribution functions. Here the estimated distribution function for each possibly right-, left-, or interval-censored observation is based on a shrinkage estimator similar to the nonparametric empirical estimator of Ghosh, Lahiri, and Tiwari (1989, Communications in Statistics--Theory and Methods 18, 121-146), and permutation is carried out within strata. The proposed test with a weighted Mann-Whitney functional is similar to the permutation form of the stratified log-rank test when there is a large strata effect or the sample size in each stratum is large and is similar to the permutation form of the ordinary log-rank test when there is little strata effect. Thus, the proposed test unifies the advantages of both the stratified and ordinary log-rank tests. By changing the functional, we may obtain a stratified Prentice-Wilcoxon test or a difference in means test with similar unifying properties. We show through simulations the advantage of the proposed test over existing tests for uncensored and right-censored data.

摘要

我们提出了一类针对分层生存数据的置换检验。这些检验是使用Fay和Shih(1998年,《美国统计协会杂志》93卷,387 - 396页)的框架推导出来的,该框架通过基于估计分布函数的一个泛函对分数进行置换来创建检验。这里,对于每个可能是右删失、左删失或区间删失的观测值,其估计分布函数基于一种收缩估计量,该估计量类似于Ghosh、Lahiri和Tiwari(1989年,《统计通信——理论与方法》18卷,121 - 146页)的非参数经验估计量,并且置换是在各层内进行的。当存在较大的层效应或每层的样本量较大时,所提出的带有加权Mann - Whitney泛函的检验类似于分层对数秩检验的置换形式;而当层效应较小时,它类似于普通对数秩检验的置换形式。因此,所提出的检验统一了分层对数秩检验和普通对数秩检验的优点。通过改变泛函,我们可以得到具有类似统一性质的分层Prentice - Wilcoxon检验或均值差异检验。我们通过模拟展示了所提出的检验相对于现有针对未删失和右删失数据的检验的优势。

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