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使用威尔科克森统计量对生存数据进行序贯监测。

Sequential monitoring of survival data with the Wilcoxon statistic.

作者信息

Lan K K, Rosenberger W F, Lachin J M

机构信息

Department of Statistics, George Washington University, Rockville, Maryland 20852, USA.

出版信息

Biometrics. 1995 Sep;51(3):1175-83.

PMID:7548701
Abstract

When a spending function is used in sequential data monitoring of a clinical trial, it is important to know the information fraction at the times of interim analysis. In a maximum duration designed study, the information fraction is unknown when data are monitored, and it has to be estimated. The modified Wilcoxon statistic developed by Peto and Peto and modified by Prentice is often used to compare two survival curves in a clinical trial. We give guidelines for estimating the information fraction in a maximum duration trial when this statistic is employed. When there is a relatively low event rate or the survival time is approximately exponential, the information fraction for the Peto-Peto-Prentice Wilcoxon statistic is very close to that of the popular logrank statistic. In other cases, it would be helpful to estimate the information fraction as a function of elapsed calendar time. We discuss both group sequential and continuous monitoring.

摘要

当在临床试验的序贯数据监测中使用支出函数时,了解中期分析时间点的信息分数非常重要。在一项设定了最长持续时间的研究中,监测数据时信息分数是未知的,必须进行估计。由佩托和佩托开发并经普伦蒂斯修改的修正威尔科克森统计量常用于临床试验中比较两条生存曲线。当采用该统计量时,我们给出了在最长持续时间试验中估计信息分数的指导原则。当事件发生率相对较低或生存时间近似呈指数分布时,佩托 - 佩托 - 普伦蒂斯威尔科克森统计量的信息分数与常用的对数秩统计量的信息分数非常接近。在其他情况下,将信息分数估计为经过的日历时间的函数会有所帮助。我们讨论了组序贯监测和连续监测两种情况。

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