Incorporating patient preferences and burden-of-disease in evaluating ALS drug candidate AMX0035: a Bayesian decision analysis perspective.

Objective: Provide US FDA and amyotrophic lateral sclerosis (ALS) society with a systematic, transparent, and quantitative framework to evaluate the efficacy of the ALS therapeutic candidate AMX0035 in its phase 2 trial, which showed statistically significant effects (-value 3%) in slowing the rate of ALS progression on a relatively small sample size of 137 patients.

Methods: We apply Bayesian decision analysis (BDA) to determine the optimal type I error rate (-value) under which the clinical evidence of AMX0035 supports FDA approval. Using rigorous estimates of ALS disease burden, our BDA framework strikes the optimal balance between FDA's need to limit adverse effects (type I error) and patients' need for expedited access to a potentially effective therapy (type II error). We apply BDA to evaluate long-term patient survival based on clinical evidence from AMX0035 and Riluzole.

Results: The BDA-optimal type I error for approving AMX0035 is higher than the 3% -value reported in the phase 2 trial if the probability of the therapy being effective is at least 30%. Assuming a 50% probability of efficacy and a signal-to-noise ratio of treatment effect between 25% and 50% (benchmark: 33%), the optimal type I error rate ranges from 2.6% to 26.3% (benchmark: 15.4%). The BDA-optimal type I error rate is robust to perturbations in most assumptions except for a probability of efficacy below 5%.

Conclusion: BDA provides a useful framework to incorporate subjective perspectives of ALS patients and objective burden-of-disease metrics to evaluate the therapeutic effects of AMX0035 in its phase 2 trial.