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Applying generalized funnel plots to help design statistical analyses

Version 2 2024-06-04, 07:41
Version 1 2023-02-09, 02:05
journal contribution
posted on 2024-06-04, 07:41 authored by J Aisbett, Eric DrinkwaterEric Drinkwater, KL Quarrie, S Woodcock
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.

History

Journal

Statistical Papers

Volume

64

Pagination

355-364

ISSN

0932-5026

eISSN

1613-9798

Language

en

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

Springer Science and Business Media LLC

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