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生物医学实验室与临床科学中的统计学:应用、问题与陷阱

Statistics in biomedical laboratory and clinical science: applications, issues and pitfalls.

作者信息

Ludbrook John

机构信息

Department of Surgery, The University of Melbourne, Melbourne, Australia.

出版信息

Med Princ Pract. 2008;17(1):1-13. doi: 10.1159/000109583.

Abstract

This review is directed at biomedical scientists who want to gain a better understanding of statistics: what tests to use, when, and why. In my view, even during the planning stage of a study it is very important to seek the advice of a qualified biostatistician. When designing and analyzing a study, it is important to construct and test global hypotheses, rather than to make multiple tests on the data. If the latter cannot be avoided, it is essential to control the risk of making false-positive inferences by applying multiple comparison procedures. For comparing two means or two proportions, it is best to use exact permutation tests rather then the better known, classical, ones. For comparing many means, analysis of variance, often of a complex type, is the most powerful approach. The correlation coefficient should never be used to compare the performances of two methods of measurement, or two measures, because it does not detect bias. Instead the Altman-Bland method of differences or least-products linear regression analysis should be preferred. Finally, the educational value to investigators of interaction with a biostatistician, before, during and after a study, cannot be overemphasized.

摘要

本综述面向那些希望更好地理解统计学的生物医学科学家

使用何种检验、何时使用以及为何使用。在我看来,即使在研究的规划阶段,寻求合格生物统计学家的建议也非常重要。在设计和分析研究时,构建并检验整体假设而非对数据进行多次检验很重要。如果无法避免后者,通过应用多重比较程序来控制做出假阳性推断的风险至关重要。为比较两个均值或两个比例,最好使用精确置换检验而非更知名的经典检验。为比较多个均值,方差分析(通常是复杂类型)是最有效的方法。相关系数绝不应被用于比较两种测量方法或两个测量指标的性能,因为它无法检测偏差。相反,应优先选择奥特曼 - 布兰德差异法或最小乘积线性回归分析。最后,研究人员在研究前、研究期间和研究后与生物统计学家互动的教育价值再怎么强调都不为过。

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