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Tests for symmetry with right censoring

机译:使用右删失进行对称性测试

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Permutation tests for symmetry are suggested using data that are subject lo right censoring. Such tests are directly relevant to the assumptions that underlie the generalized Wilcoxon tesl since the symmetric logistic distribution for log-errors has been used to motivate Wilcoxon scores in the censored accelerated failure time model. Its principal competitor is the log-rank (LGR) test motivated by an extreme value error distribution that is positively skewed. The proposed one-sided tests for symmetry against the alternative of positive skewness are directly relevant to the choice between usage of these two tests. The permutation tests use statistics from the weighted LGR class normally used for making two-sample comparisons. From this class, the test using LGR weights (all weights equal) showed the greatest discriminatory power in simulations that compared the possibility of logistic errors versus extreme value errors. In the test construction, a median estimate, determined by inverting the Kaplan-Meier estimator, is used to divide the data into a "control" group to its left that is compared with a "treatment" group to its right. As an unavoidable consequence of testing symmetry, data in the control group that have been censored become uninformative in performing this two-sample test. Thus, early heavy censoring of data can reduce the effective sample size of the control group and result in diminished power for discriminating symmetry in the population distribution.
机译:建议使用主题右检查的数据进行对称性的置换测试。此类检验与广义Wilcoxon tesl所基于的假设直接相关,因为针对对数错误的对称逻辑对数分布已用于激励审查的加速故障时间模型中的Wilcoxon分数。它的主要竞争对手是对数误差(LGR)测试,其动机是正偏的极值误差分布。所提议的针对正偏度的对称性的单方面测试与这两种测试的使用之间的选择直接相关。排列检验使用加权LGR类的统计信息,通常用于进行两个样本的比较。在此类中,使用LGR权重(所有权重均相等)进行的测试在模拟中比较了逻辑误差和极值误差的可能性,显示出最大的区分能力。在测试构造中,通过反转Kaplan-Meier估计器确定的中值估计值用于将数据分为左侧的“对照组”和右侧的“治疗”组。作为测试对称性不可避免的结果,已被检查的对照组数据在进行此两样本测试时变得无用。因此,尽早对数据进行大量审查会减少对照组的有效样本量,并导致区分人群分布对称性的能力降低。

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