Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 1900-01-01 DOI:10.2202/1557-4679.1018

J. A. Ferreira, A. Zwinderman

引用次数: 25

Abstract

We provide a method for calculating the sample size required to attain a given average power (the ratio of rejected hypotheses to the number of false hypotheses) and a given false discovery rate (the number of incorrect rejections divided by the number of rejections) in adaptive versions of the Benjamini-Hochberg method of multiple testing. The method works in an asymptotic sense as the number of hypotheses grows to infinity and under quite general conditions, and it requires data from a pilot study. The consistency of the method follows from several results in classical areas of nonparametric statistics developed in a new context of "weak" dependence.

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用Benjamini-Hochberg方法计算近似功率和样本量

在benjamin - hochberg多重检验方法的自适应版本中，我们提供了一种方法来计算获得给定平均功率(被拒绝的假设与错误假设的数量之比)和给定错误发现率(不正确拒绝的数量除以拒绝的数量)所需的样本量。当假设的数量增长到无穷大，并且在相当一般的条件下，该方法在渐近意义上起作用，并且它需要来自初步研究的数据。该方法的一致性来自非参数统计经典领域的几个结果，这些结果是在“弱”依赖的新背景下发展起来的。

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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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.