Literature DB >> 36125120

The ellipse of insignificance, a refined fragility index for ascertaining robustness of results in dichotomous outcome trials.

David Robert Grimes1,2.   

Abstract

There is increasing awareness throughout biomedical science that many results do not withstand the trials of repeat investigation. The growing abundance of medical literature has only increased the urgent need for tools to gauge the robustness and trustworthiness of published science. Dichotomous outcome designs are vital in randomized clinical trials, cohort studies, and observational data for ascertaining differences between experimental and control arms. It has however been shown with tools like the fragility index (FI) that many ostensibly impactful results fail to materialize when even small numbers of patients or subjects in either the control or experimental arms are recoded from event to non-event. Critics of this metric counter that there is no objective means to determine a meaningful FI. As currently used, FI is not multidimensional and is computationally expensive. In this work, a conceptually similar geometrical approach is introduced, the ellipse of insignificance. This method yields precise deterministic values for the degree of manipulation or miscoding that can be tolerated simultaneously in both control and experimental arms, allowing for the derivation of objective measures of experimental robustness. More than this, the tool is intimately connected with sensitivity and specificity of the event/non-event tests, and is readily combined with knowledge of test parameters to reject unsound results. The method is outlined here, with illustrative clinical examples.
© 2022, Grimes.

Entities:  

Keywords:  epidemiology; global health; medicine; meta-research; none; reproducibility; scientific research; statistics; trustworthiness

Year:  2022        PMID: 36125120      PMCID: PMC9586556          DOI: 10.7554/eLife.79573

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


  30 in total

1.  The unit fragility index: an additional appraisal of "statistical significance" for a contrast of two proportions.

Authors:  A R Feinstein
Journal:  J Clin Epidemiol       Date:  1990       Impact factor: 6.437

2.  Measurement error and the replication crisis.

Authors:  Eric Loken; Andrew Gelman
Journal:  Science       Date:  2017-02-10       Impact factor: 47.728

Review 3.  The statistical significance of randomized controlled trial results is frequently fragile: a case for a Fragility Index.

Authors:  Michael Walsh; Sadeesh K Srinathan; Daniel F McAuley; Marko Mrkobrada; Oren Levine; Christine Ribic; Amber O Molnar; Neil D Dattani; Andrew Burke; Gordon Guyatt; Lehana Thabane; Stephen D Walter; Janice Pogue; P J Devereaux
Journal:  J Clin Epidemiol       Date:  2014-02-05       Impact factor: 6.437

4.  The fragility of statistically significant findings from randomized trials in spine surgery: a systematic survey.

Authors:  Nathan Evaniew; Carly Files; Christopher Smith; Mohit Bhandari; Michelle Ghert; Michael Walsh; Philip J Devereaux; Gordon Guyatt
Journal:  Spine J       Date:  2015-06-11       Impact factor: 4.166

5.  The Fragility Index in Randomized Clinical Trials as a Means of Optimizing Patient Care.

Authors:  Christopher J Tignanelli; Lena M Napolitano
Journal:  JAMA Surg       Date:  2019-01-01       Impact factor: 14.766

Review 6.  An investigation of the false discovery rate and the misinterpretation of p-values.

Authors:  David Colquhoun
Journal:  R Soc Open Sci       Date:  2014-11-19       Impact factor: 2.963

7.  Oxygen diffusion in ellipsoidal tumour spheroids.

Authors:  David Robert Grimes; Frederick J Currell
Journal:  J R Soc Interface       Date:  2018-08       Impact factor: 4.118

8.  The new normal? Redaction bias in biomedical science.

Authors:  David Robert Grimes; James Heathers
Journal:  R Soc Open Sci       Date:  2021-12-01       Impact factor: 2.963

9.  Correction of scientific literature: Too little, too late!

Authors:  Lonni Besançon; Elisabeth Bik; James Heathers; Gideon Meyerowitz-Katz
Journal:  PLoS Biol       Date:  2022-03-03       Impact factor: 8.029

10.  Why most published research findings are false.

Authors:  John P A Ioannidis
Journal:  PLoS Med       Date:  2005-08-30       Impact factor: 11.613

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