Clinical trials in critical care : can a Bayesian approach enhance clinical and scientific decision making?

Recent Bayesian reanalyses of prominent trials in critical illness have generated controversy by contradicting the initial frequentist conclusions. Many clinicians may be skeptical that Bayesian analysis, a philosophical and statistical approach that combines prior beliefs with data to generate probabilities, provides more useful information about the meaning of clinical trials than the conventional frequentist approach. In this Personal View we introduce clinicians to the rationale, process, and interpretation of Bayesian analysis through a systematic review and reanalysis of interventional trials in critical illness. In the majority of cases, Bayesian and frequentist analyses agreed. In the remainder, Bayesian analysis identified interventions where benefit was probable despite the absence of statistical significance, where interpretation depended substantially on choice of prior distribution, and where benefit was improbable despite statistical significance. Bayesian analysis in critical care medicine can help distinguish harm from uncertainty and establish the probability of clinically important benefit for clinicians, policymakers, and patients