Applying generalized funnel plots to help design statistical analyses

Aisbett, J; Drinkwater, EJ; Quarrie, KL; Woodcock, S

Applying generalized funnel plots to help design statistical analyses

Aisbett, J Drinkwater, EJ Quarrie, KL Woodcock, S

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Statistical Papers

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Field	Value	Language
dc.contributor.author	Aisbett, J
dc.contributor.author	Drinkwater, EJ
dc.contributor.author	Quarrie, KL
dc.contributor.author	Woodcock, S https://orcid.org/0000-0003-4903-3164
dc.date.accessioned	2022-05-25T13:35:22Z
dc.date.available	2022-05-25T13:35:22Z
dc.identifier.citation	Statistical Papers
dc.identifier.issn	0932-5026
dc.identifier.issn	1613-9798
dc.identifier.uri	http://hdl.handle.net/10453/157698
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>Researchers across many fields routinely analyze trial data using Null Hypothesis Significance Tests with zero null and p < 0.05. To promote thoughtful statistical testing, we propose a visualization tool that highlights practically meaningful effects when calculating sample sizes. The tool re-purposes and adapts funnel plots, originally developed for meta-analyses, after generalizing them to cater for meaningful effects. As with traditional sample size calculators, researchers must nominate anticipated effect sizes and variability alongside the desired power. The advantage of our tool is that it simultaneously presents sample sizes needed to adequately power tests for equivalence, for non-inferiority and for superiority, each considered at up to three alpha levels and in positive and negative directions. The tool thus encourages researchers at the design stage to think about the type and level of test in terms of their research goals, costs of errors, meaningful effect sizes and feasible sample sizes. An R-implementation of the tool is available on-line.</jats:p>
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Statistical Papers
dc.relation.isbasedon	10.1007/s00362-022-01322-y
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0104 Statistics
dc.subject.classification	Statistics & Probability
dc.title	Applying generalized funnel plots to help design statistical analyses
dc.type	Journal Article
utslib.for	0104 Statistics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2022-05-25T13:35:21Z
pubs.publication-status	Published online

Abstract:

AbstractResearchers across many fields routinely analyze trial data using Null Hypothesis Significance Tests with zero null and p < 0.05. To promote thoughtful statistical testing, we propose a visualization tool that highlights practically meaningful effects when calculating sample sizes. The tool re-purposes and adapts funnel plots, originally developed for meta-analyses, after generalizing them to cater for meaningful effects. As with traditional sample size calculators, researchers must nominate anticipated effect sizes and variability alongside the desired power. The advantage of our tool is that it simultaneously presents sample sizes needed to adequately power tests for equivalence, for non-inferiority and for superiority, each considered at up to three alpha levels and in positive and negative directions. The tool thus encourages researchers at the design stage to think about the type and level of test in terms of their research goals, costs of errors, meaningful effect sizes and feasible sample sizes. An R-implementation of the tool is available on-line.

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http://hdl.handle.net/10453/157698