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000140670 037__ $$aDZNE-2020-06992
000140670 041__ $$aEnglish
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000140670 1001_ $$aPiper, Sophie K$$b0
000140670 245__ $$aExact replication: Foundation of science or game of chance?
000140670 260__ $$aLawrence, KS$$bPLoS$$c2019
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000140670 520__ $$aThe need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%-in essence, a coin toss. Accordingly, we use the provocative analogy of 'replicating' a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches.
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000140670 650_2 $$2MeSH$$aAnimals
000140670 650_2 $$2MeSH$$aBayes Theorem
000140670 650_2 $$2MeSH$$aBiomedical Research: methods
000140670 650_2 $$2MeSH$$aBiomedical Research: statistics & numerical data
000140670 650_2 $$2MeSH$$aData Interpretation, Statistical
000140670 650_2 $$2MeSH$$aHumans
000140670 650_2 $$2MeSH$$aModels, Statistical
000140670 650_2 $$2MeSH$$aProbability
000140670 650_2 $$2MeSH$$aPublications
000140670 650_2 $$2MeSH$$aReproducibility of Results
000140670 650_2 $$2MeSH$$aResearch Design: statistics & numerical data
000140670 650_2 $$2MeSH$$aSample Size
000140670 7001_ $$aGrittner, Ulrike$$b1
000140670 7001_ $$0P:(DE-HGF)0$$aRex, Andre$$b2
000140670 7001_ $$aRiedel, Nico$$b3
000140670 7001_ $$aFischer, Felix$$b4
000140670 7001_ $$aNadon, Robert$$b5
000140670 7001_ $$aSiegerink, Bob$$b6
000140670 7001_ $$0P:(DE-2719)2810838$$aDirnagl, Ulrich$$b7$$eLast author$$udzne
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000140670 773__ $$0PERI:(DE-600)2126773-X$$a10.1371/journal.pbio.3000188$$gVol. 17, no. 4, p. e3000188 -$$n4$$pe3000188$$q17:4<e3000188 -$$tPLoS biology$$v17$$x1545-7885$$y2019
000140670 8564_ $$uhttps://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000188
000140670 8567_ $$2Pubmed Central$$uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456162
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