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2018 ; 13
(4
): e0195474
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Bayesian reanalysis of null results reported in medicine: Strong yet variable
evidence for the absence of treatment effects
#MMPMID29694370
Hoekstra R
; Monden R
; van Ravenzwaaij D
; Wagenmakers EJ
PLoS One
2018[]; 13
(4
): e0195474
PMID29694370
show ga
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 test. Here we reanalyzed a subset of
43 null results from 36 articles using a default Bayesian test for contingency
tables. This Bayesian reanalysis revealed that, on average, the reported null
results provided strong evidence for the absence of an effect. However, the
degree of this evidence is variable and cannot be reliably predicted from the
p-value. For null results, sample size is a better (albeit imperfect) predictor
for the strength of evidence in favor of the null hypothesis. Together, our
findings suggest that (a) the reported null results generally correspond to
strong evidence in favor of the null hypothesis; (b) a Bayesian hypothesis test
can provide additional information to assist the interpretation of null results.