Why the study?
False findings are an inevitable consequence of statistical testing in randomised controlled trials in anaesthesia and critical care, but the extent of false positive and false negative risks is unclear.
False findings are an inevitable consequence of statistical testing. In this article, I use Bayes' theorem to estimate the false positive and false negative risks for randomised controlled trials related to our speciality. For small trials in peri-operative medicine, the false positive risk appears to be at least 50%. For trials reporting weakly significant p values, the risk is even higher. By contrast, large, multicentre trials in critical care appear to have a high false negative risk. These findings suggest much of the evidence that underpins our clinical practice is likely to be wrong.
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David Andrew Sidebotham (2020) studied Critical illness (shock, sepsis, acute respiratory failure) (n=6,968). Distributional frailty imbalance (Clinical Frailty Scale) vs. Balanced frailty distribution was evaluated on Proportion of simulated trials with frailty imbalance generating ≥2% difference in hospital mortality.
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