Statistically significant differences versus convincing evidence of real treatment effects: an analysis of the false positive risk for single-centre trials in anaesthesia

Abstract

BackgroundThe American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may ‘supplement or even replace P-values’. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. MethodsWe reviewed single-centre, randomised trials in 10 anaesthesia journals over 6 yr where differences in a primary binary outcome were statistically significant. We calculated a Bayes factor by two methods (Gunel, Kass). From the Bayes factor we calculated the FPR for different prior beliefs for a real treatment effect. Prior beliefs were quantified by assigning pretest probabilities to the null and alternative hypotheses. ResultsFor equal pretest probabilities of 0.5, the median (inter-quartile range [IQR]) FPR was 6% (1–22%) by the Gunel method and 6% (1–19%) by the Kass method. One in five trials had an FPR ≥20%. For trials reporting P-values 0.01–0.05, the median (IQR) FPR was 25% (16–30%) by the Gunel method and 20% (16–25%) by the Kass method. More than 90% of trials reporting P-values 0.01–0.05 required a pretest probability >0.5 to achieve an FPR of 5%. The median (IQR) difference in the FPR calculated by the two methods was 0% (0–2%). ConclusionsOur findings suggest that a substantial proportion of single-centre trials in anaesthesia reporting statistically significant differences provide limited evidence of real treatment effects, or, alternatively, required an implausibly high prior belief in a real treatment effect. Clinical trial registrationPROSPERO (CRD42023350783).