Why Bayesian analysis of clinical trials may be a good idea– a worked example of the aducanumab studies

Abstract

AbstractBackgroundThe FDA decision to approve Aducanumab has sparked controversy. Here, we argue that some of these controversies are rooted in the conclusions facilitated by classical frequentist analysis. We suggest that a Bayesian analysis would answer some of the burning questions the aducanumab trials have posed to the field.MethodWe applied Bayesian analysis of model plausibility and effect sizes based on simulated data (the original data are not publicly available) of the two phase 3 trials (trials 301 and 302) of aducanumab in prodromal and mild dementia stages of Alzheimer’s disease.ResultExcluding the rapid progressors from our simulated data did not produce decisive evidence supporting aducanumab’s treatment effect. The Bayesian analysis suggested that the study indicator (study 301 or study 302) was irrelevant as a predictor of treatment effect (Bayes Factor (BF)exc = 42.16). Our simulated data further offered overwhelming evidence against Biogen’s assumption that rapid progressors affected any treatment arm of either study disproportionately (i.e., BFexc = 9910.74). We also found anecdotal evidence against the treatment arms (i.e., BFexc = 2.65) as a predictor of CDR‐SB change (Figure 1). The prior and posterior plots show that if the effect existed, it would be very small, likely ranging from Kendall’s tau 0.007 to 0.052, with a median of 0.029 (Figure 2).ConclusionBayesian analysis can quantify evidence in favor of the presence or absence of an effect, as well as assess the strength of the effect. In the case of our simulated data, Bayesian analysis quantified evidence that studies 301 and 302 do not provide diverging evidence, and that the treatment arm‐specific rapid progressor effect was absent. The Bayesian analysis thus provides more informative and nuanced conclusions than the reported frequentist tests, which leave many questions unanswered.