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[医学研究中的统计检验:传统方法与多元非参数置换检验]

[Statistical tests in medical research: traditional methods vs. multivariate NPC permutation tests].

作者信息

Arboretti Rosa, Bordignon Paolo, Corain Livio, Palermo Giuseppe, Pesarin Fortunato, Salmaso Luigi

机构信息

1Department of Management and Engineering, Università di Padova, Vicenza - Italy.

出版信息

Urologia. 2015 Apr-Jun;82(2):130-6. doi: 10.5301/uro.5000117. Epub 2015 Apr 6.

DOI:10.5301/uro.5000117
PMID:25907894
Abstract

Statistical tests in medical research: traditional methods vs. multivariate npc permutation tests.Within medical research, a useful statistical tool is based on hypotheses testing in terms of the so-called null, that is the treatment has no effect, and alternative hypotheses, that is the treatment has some effects. By controlling the risks of wrong decisions, empirical data are used in order to possibly reject the null hypotheses in favour of the alternative, so that demonstrating the efficacy of a treatment of interest. The multivariate permutation tests, based on the nonparametric combination - NPC method, provide an innovative, robust and effective hypotheses testing solution to many real problems that are commonly encountered in medical research when multiple end-points are observed. This paper discusses the various approaches to hypothesis testing and the main advantages of NPC tests, which consist in the fact that they require much less stringent assumptions than traditional statistical tests. Moreover, the related results may be extended to the reference population even in case of selection-bias, that is non-random sampling. In this work, we review and discuss some basic testing procedures along with the theoretical and practical relevance of NPC tests showing their effectiveness in medical research. Within the non-parametric methods, NPC tests represent the current "frontier" of statistical research, but already widely available in the practice of analysis of clinical data.

摘要

医学研究中的统计检验

传统方法与多元非参数组合(NPC)置换检验。在医学研究中,一种有用的统计工具基于在所谓的原假设(即治疗无效果)和备择假设(即治疗有某些效果)方面的假设检验。通过控制错误决策的风险,使用经验数据以便可能拒绝原假设而支持备择假设,从而证明感兴趣的治疗方法的疗效。基于非参数组合(NPC)方法的多元置换检验为医学研究中观察到多个终点时常见的许多实际问题提供了一种创新、稳健且有效的假设检验解决方案。本文讨论了假设检验的各种方法以及NPC检验的主要优点,其优点在于与传统统计检验相比,它们所需的假设条件宽松得多。此外,即使在存在选择偏倚(即非随机抽样)的情况下,相关结果也可扩展到参考人群。在这项工作中,我们回顾并讨论了一些基本检验程序以及NPC检验的理论和实际相关性,展示了它们在医学研究中的有效性。在非参数方法中,NPC检验代表了统计研究的当前“前沿”,但在临床数据分析实践中已广泛可用。

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