Bayesian analysis of real-world data as evidence for drug approval: Remembering Sir Michael Rawlins


Balázs Szigeti, Lawrence D. Phillips and David Nutt


July 17, 2023


The two pillars of modern medical research are where in most randomized controlled trials (RCTs), the active treatment is compared with placebo. A recent expert consensus survey endorsed the statement that ‘Results from placebo-controlled trials are more reliable than results from any other study design’,1 reflecting that placebo controlled RCTs are considered to be the gold standard. However, in his 2008 Harvein Oration, the prestigious annual lecture of the Royal College of Physicians of London, Sir Michael Rawlins, the ex-head of the National Institute for Health and Care Excellence (NICE) and the Medicines and Healthcare products Regulatory Agency (MHRA), pointed out RCTs are not the apex of evidence, but rather a piece of a larger evidence puzzle. He wrote, ‘RCTs are often called the ‘gold standard’ for demonstrating (or refuting) the benefits of a particular intervention. Yet the technique has important limitations of which four are particularly troublesome: the null hypothesis, probability, generalisability and resource implications’.2 Here, we follow the footsteps of Sir Michael Rawlins and highlight how the combination of real-world evidence (RWE) and Bayesian probability analyses could complement the traditional approach of RCTs and null hypothesis significance testing (NHST).

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