Evaluation of the statistical power for multiple tests: a case study.

by: Adeline Yeo, Yongming Qu

Pharmaceutical statistics (31 March 2008)

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Abstract

It is challenging to estimate the statistical power when a complicated testing strategy is used to adjust for the type-I error for multiple comparisons in a clinical trial. In this paper, we use the Bonferroni Inequality to estimate the lower bound of the statistical power assuming that test statistics are approximately normally distributed and the correlation structure among test statistics is unknown or only partially known. The method was applied to the design of a clinical study for sample size and statistical power estimation. Copyright (c) 2008 John Wiley & Sons, Ltd.

@article{citeulike:2665282, abstract = {It is challenging to estimate the statistical power when a complicated testing strategy is used to adjust for the type-I error for multiple comparisons in a clinical trial. In this paper, we use the Bonferroni Inequality to estimate the lower bound of the statistical power assuming that test statistics are approximately normally distributed and the correlation structure among test statistics is unknown or only partially known. The method was applied to the design of a clinical study for sample size and statistical power estimation. Copyright (c) 2008 John Wiley \& Sons, Ltd.}, address = {Eli Lilly and Company, Indianapolis, IN 48285, USA.}, author = {Yeo, Adeline and Qu, Yongming }, citeulike-article-id = {2665282}, doi = {http://dx.doi.org/10.1002/pst.319}, issn = {1539-1612}, journal = {Pharmaceutical statistics}, keywords = {multiplicity, power}, month = {March}, posted-at = {2008-04-14 05:20:28}, priority = {2}, title = {Evaluation of the statistical power for multiple tests: a case study.}, url = {http://dx.doi.org/10.1002/pst.319}, year = {2008} }

RIS record

TY - JOUR ID - citeulike:2665282 L3 - citeulike-article-id:2665282 TI - Evaluation of the statistical power for multiple tests: a case study. JF - Pharmaceutical statistics AD - Eli Lilly and Company, Indianapolis, IN 48285, USA. SN - 1539-1612 N2 - It is challenging to estimate the statistical power when a complicated testing strategy is used to adjust for the type-I error for multiple comparisons in a clinical trial. In this paper, we use the Bonferroni Inequality to estimate the lower bound of the statistical power assuming that test statistics are approximately normally distributed and the correlation structure among test statistics is unknown or only partially known. The method was applied to the design of a clinical study for sample size and statistical power estimation. Copyright (c) 2008 John Wiley & Sons, Ltd. KW - multiplicity KW - power AU - Yeo, Adeline AU - Qu, Yongming PY - 2008/03/31/ UR - http://dx.doi.org/10.1002/pst.319 ER -

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