Use of Bayesian decision analysis in the design of patient-centered clinical trials for kidney failure devices

Integrating patient preferences into the design of randomized clinical trials (RCTs) may help accelerate innovation for alternative kidney replacement therapy by appropriately selecting a trial's significance level and sample size, and have a meaningful impact on people suffering from kidney failure. While a conventional one-sided significance level threshold of 2.5 % is often used to assess the safety of a proposed device, we show in this study that it is not necessarily consistent with the risk-preferences of patients with dialysis-dependent kidney disease. We apply a Bayesian decision analysis (BDA) framework to results from a patient preference survey and estimate the optimal significance level and sample size required in an RCT to assess the safety of a hypothetical dialysis device. Based on survey responses from 599 patients with dialysis-dependent kidney failure, we found that the optimal significance level threshold differs significantly from the classical 2.5 % threshold used in two-sided hypothesis tests across various patient subgroups. On average, patients tended to require a significance level of 1.2 % for the risk of bleeding and a significance level <0.1 % for the risk of serious infection, suggesting that the survey respondents were not willing to bear either type of additional risk presented by the hypothetical device in exchange for the possible benefits described in the survey. However, there was heterogeneity among the patient subgroups of dialysis modality, age, gender, ethnicity, and time on dialysis. Overall, our study shows that the BDA framework is a robust, systematic, transparent, and reproducible method for incorporating patient preference information into the design and regulatory review process of clinical trials for novel therapeutics.