Background: In December 2019, in light of additional blinded data, Biogen claimed efficacy of the drug Aducanumab (ADU). Objective: We conducted a reanalysis of the phase III ADU summary statistics, focusing in particular on the Clinical Dementia Rating-Sum of Boxes. Methods: We used a Bayesian framework to mitigate the problems of the null-hypothesis significance testing framework. In particular, we used Bayes Factor (BF) to analyze the summary statistics. The BF is the comparison of how well two hypotheses predict the data. Results: Our results showed that the evidence for ADU efficacy is very low. The results show that the only data with a BF value in favor of the alternative hypothesis (i.e., drug efficacy) is the high-dose condition in the EMERGE trial. However, the obtained BF falls within the range of values considered anecdotal, meaning a low level of evidence. Conclusion: We provide a clearer interpretation of the results of the clinical trials based on the Bayesian framework, as this may be useful for future development and research in the field.
A Bayesian Reanalysis of the Phase III Aducanumab (ADU) Trial
Tommaso Costa
First
;Franco CaudaLast
2022-01-01
Abstract
Background: In December 2019, in light of additional blinded data, Biogen claimed efficacy of the drug Aducanumab (ADU). Objective: We conducted a reanalysis of the phase III ADU summary statistics, focusing in particular on the Clinical Dementia Rating-Sum of Boxes. Methods: We used a Bayesian framework to mitigate the problems of the null-hypothesis significance testing framework. In particular, we used Bayes Factor (BF) to analyze the summary statistics. The BF is the comparison of how well two hypotheses predict the data. Results: Our results showed that the evidence for ADU efficacy is very low. The results show that the only data with a BF value in favor of the alternative hypothesis (i.e., drug efficacy) is the high-dose condition in the EMERGE trial. However, the obtained BF falls within the range of values considered anecdotal, meaning a low level of evidence. Conclusion: We provide a clearer interpretation of the results of the clinical trials based on the Bayesian framework, as this may be useful for future development and research in the field.File | Dimensione | Formato | |
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