A nomogram for <it>P </it>values

<p>Abstract</p> <p>Background</p> <p><it>P </it>values are the most commonly used tool to measure evidence against a hypothesis. Several attempts have been made to transform <it>P </it>values to minimum Bayes factors and minimum posterior probabi...

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Main Author: Held Leonhard
Format: Article
Language:English
Published: BMC 2010-03-01
Series:BMC Medical Research Methodology
Online Access:http://www.biomedcentral.com/1471-2288/10/21
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spelling doaj-0d85392100e2497087f17098e0d3035e2020-11-25T00:18:44ZengBMCBMC Medical Research Methodology1471-22882010-03-011012110.1186/1471-2288-10-21A nomogram for <it>P </it>valuesHeld Leonhard<p>Abstract</p> <p>Background</p> <p><it>P </it>values are the most commonly used tool to measure evidence against a hypothesis. Several attempts have been made to transform <it>P </it>values to minimum Bayes factors and minimum posterior probabilities of the hypothesis under consideration. However, the acceptance of such calibrations in clinical fields is low due to inexperience in interpreting Bayes factors and the need to specify a prior probability to derive a lower bound on the posterior probability.</p> <p>Methods</p> <p>I propose a graphical approach which easily translates any prior probability and <it>P </it>value to minimum posterior probabilities. The approach allows to visually inspect the dependence of the minimum posterior probability on the prior probability of the null hypothesis. Likewise, the tool can be used to read off, for fixed posterior probability, the maximum prior probability compatible with a given <it>P </it>value. The maximum <it>P </it>value compatible with a given prior and posterior probability is also available.</p> <p>Results</p> <p>Use of the nomogram is illustrated based on results from a randomized trial for lung cancer patients comparing a new radiotherapy technique with conventional radiotherapy.</p> <p>Conclusion</p> <p>The graphical device proposed in this paper will enhance the understanding of <it>P </it>values as measures of evidence among non-specialists.</p> http://www.biomedcentral.com/1471-2288/10/21
collection DOAJ
language English
format Article
sources DOAJ
author Held Leonhard
spellingShingle Held Leonhard
A nomogram for <it>P </it>values
BMC Medical Research Methodology
author_facet Held Leonhard
author_sort Held Leonhard
title A nomogram for <it>P </it>values
title_short A nomogram for <it>P </it>values
title_full A nomogram for <it>P </it>values
title_fullStr A nomogram for <it>P </it>values
title_full_unstemmed A nomogram for <it>P </it>values
title_sort nomogram for <it>p </it>values
publisher BMC
series BMC Medical Research Methodology
issn 1471-2288
publishDate 2010-03-01
description <p>Abstract</p> <p>Background</p> <p><it>P </it>values are the most commonly used tool to measure evidence against a hypothesis. Several attempts have been made to transform <it>P </it>values to minimum Bayes factors and minimum posterior probabilities of the hypothesis under consideration. However, the acceptance of such calibrations in clinical fields is low due to inexperience in interpreting Bayes factors and the need to specify a prior probability to derive a lower bound on the posterior probability.</p> <p>Methods</p> <p>I propose a graphical approach which easily translates any prior probability and <it>P </it>value to minimum posterior probabilities. The approach allows to visually inspect the dependence of the minimum posterior probability on the prior probability of the null hypothesis. Likewise, the tool can be used to read off, for fixed posterior probability, the maximum prior probability compatible with a given <it>P </it>value. The maximum <it>P </it>value compatible with a given prior and posterior probability is also available.</p> <p>Results</p> <p>Use of the nomogram is illustrated based on results from a randomized trial for lung cancer patients comparing a new radiotherapy technique with conventional radiotherapy.</p> <p>Conclusion</p> <p>The graphical device proposed in this paper will enhance the understanding of <it>P </it>values as measures of evidence among non-specialists.</p>
url http://www.biomedcentral.com/1471-2288/10/21
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