多端点的dunnett型过程

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI:10.2202/1557-4679.1258

M. Hasler, L. Hothorn

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引用次数: 26

摘要

本文描述了一种同时对多个终点进行几种处理与对照比较的方法。假设这些端点是正态分布，具有不同的尺度和方差。近似的多变量t分布用于获得测试决策的分位数，多重调整的p值和同时置信区间。仿真结果表明，该方法在可接受的范围内控制了比较点和端点的类误差。该方法将应用于一项随机临床试验，比较两组新的体外循环与三个主要终点的标准。相关的R包是可用的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Dunnett-Type Procedure for Multiple Endpoints

This paper describes a method for comparisons of several treatments with a control, simultaneously for multiple endpoints. These endpoints are assumed to be normally distributed with different scales and variances. An approximate multivariate t-distribution is used to obtain quantiles for test decisions, multiplicity-adjusted p-values, and simultaneous confidence intervals. Simulation results show that this approach controls the family-wise error type I over both the comparisons and the endpoints in an admissible range. The approach will be applied to a randomized clinical trial comparing two new sets of extracorporeal circulations with a standard for three primary endpoints. A related R package is available.

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.