The P Value Line Dance: When Does the Music Stop?

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ID: 113323
2020
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Abstract
When should a trial stop? Such a seemingly innocent question evokes concerns of type I and II errors among those who believe that certainty can be the product of uncertainty and among researchers who have been told that they need to carefully calculate sample sizes, consider multiplicity, and not spend P values on interim analyses. However, the endeavor to dichotomize evidence into significant and nonsignificant has led to the basic driving force of science, namely uncertainty, to take a back seat. In this viewpoint we discuss that if testing the null hypothesis is the ultimate goal of science, then we need not worry about writing protocols, consider ethics, apply for funding, or run any experiments at all—all null hypotheses will be rejected at some point—everything has an effect. The job of science should be to unearth the uncertainties of the effects of treatments, not to test their difference from zero. We also show the fickleness of P values, how they may one day point to statistically significant results; and after a few more participants have been recruited, the once statistically significant effect suddenly disappears. We show plots which we hope would intuitively highlight that all assessments of evidence will fluctuate over time. Finally, we discuss the remedy in the form of Bayesian methods, where uncertainty leads; and which allows for continuous decision making to stop or continue recruitment, as new data from a trial is accumulated. [J Med Internet Res 2020;22(8):e21345]
Reference Key
bendtsen2020journalthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marcus Bendtsen;
Journal Journal of medical Internet research
Year 2020
DOI
doi:10.2196/21345
URL
https://www.jmir.org/2020/8/e21345
Keywords
medical Internet research medicine journal ehealth JMIR open access publishing medical research medical informaticssample size;randomized controlled trial;bayesian analysis;p value;dichotomization;dichotomy;error;uncertainty

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