2018
DOI: 10.1371/journal.pone.0195474
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Bayesian reanalysis of null results reported in medicine: Strong yet variable evidence for the absence of treatment effects

Abstract: Efficient medical progress requires that we know when a treatment effect is absent. We considered all 207 Original Articles published in the 2015 volume of the New England Journal of Medicine and found that 45 (21.7%) reported a null result for at least one of the primary outcome measures. Unfortunately, standard statistical analyses are unable to quantify the degree to which these null results actually support the null hypothesis. Such quantification is possible, however, by conducting a Bayesian hypothesis t… Show more

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Cited by 43 publications
(56 citation statements)
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“…Besides providing p-values to express the rejection of a null hypothesis, extra information is provided by the Bayes factor (BF) about the strength of the evidence in favor of the alternative hypothesis over the null hypothesis (BF 10 ) or vice versa [44,45,46].…”
Section: Methodsmentioning
confidence: 99%
“…Besides providing p-values to express the rejection of a null hypothesis, extra information is provided by the Bayes factor (BF) about the strength of the evidence in favor of the alternative hypothesis over the null hypothesis (BF 10 ) or vice versa [44,45,46].…”
Section: Methodsmentioning
confidence: 99%
“…In comparison, a Bayes factor of BF 10 = 1/5 (or the inverse of 5), means the observed data are five times more likely to have occurred under the null hypothesis than under the alternative hypothesis. 1 The application of the JZS Bayes factor for a large-scale reanalysis of published results is not without precedent (Hoekstra, Monden, van Ravenzwaaij, & Wagenmakers, 2018). We build upon the work of Hoekstra and colleagues in taking the results of such a Bayesian reanalysis as a starting point for selecting replication targets (for a similar approach, see Pittelkow, Hoekstra, & van Ravenzwaaij, 2019).…”
Section: Statistical Considerationsmentioning
confidence: 99%
“…The nullhypothesis-significance-testing procedure was complemented with the estimation of Bayes factors for null results, as delivered by JASP (JASP Team, 2020). Bayes factors for null results [null/alternative]) indicate the likelihood of observing the null data under the null hypothesis compared to the alternative hypothesis, thus quantifying the strength of evidence in favor of absent effects (Hoekstra et al, 2018;Wagenmakers et al, 2018). A rule of thumb is to consider a Bayes factor between 1 and 3 as weak evidence, though favoring the null hypothesis, between 3 and 10 as substantial, between 10 and 30 strong, and between 30 and 100 as decisive evidence (Jarosz and Wiley, 2014).…”
Section: Discussionmentioning
confidence: 99%