Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks

Qingyang Xu; Shomesh Chaudhuri; Danying Xiao; Andrew W Lo

Similar to , we develop a Bayesian patient-centered decision model for RCT approval which minimizes the expected loss (or harm) incurred on the patients by optimally balancing the losses of Type I and Type II errors. Here the loss does not refer to financial costs afforded by the patients, but rather the loss in patient value (i.e. how much patients weigh the relative harms of infection and death). We assign the losses per patient of being susceptible, infected, and deceased. Since Bayesian decision thresholds are invariant under the rescaling of the losses, we normalize by setting the loss per patient infection 1 . We then assign the loss per patient death relative to as , and the loss due to susceptibility to the disease as . The parameter values we assume, summarized in Table 1 , are meant to represent one reasonable valuation of the relative losses. However, in practice patient value will differ from one patient group another, especially given the large variability of mortality rate of COVID-19 in different age groups (Onder et al., 2020) . Here we report the main results of optimal sample size and statistical significance (Table 3 and 4) assuming 100. The results for 10 are provided in Supplementary Materials.