Interpreting the results of clinical trials, embracing uncertainty: A Bayesian approach

Most clinical trials use null hypothesis significance testing with frequentist statistical inference to report P values and confidence intervals for effect estimates. This method leads to a dichotomisation of results as 'significant' or 'non-significant'. A more nuanced interpretation may often be considered and in particular when the majority of the confidence interval for the effect estimate suggests benefit or harm. In contrast to the frequentist dichotomised approach based on a P value, the application of Bayesian statistics allocates credibility to a continuous spectrum of possibilities and for this reason a Bayesian approach to inference is often warranted as it will incorporate uncertainty when updating our current belief with information from a new trial. The use of Bayesian statistics is introduced in this paper for a hypothetical sepsis trial with worked examples in the R language for Statistical Computing environment and the open-source statistical software JASP. It is hoped that this general introduction to Bayesian inference stimulates some interest and confidence among clinicians to consider applying these methods to the interpretation of new evidence for interventions relevant to anaesthesia and intensive care medicine.