Estimating the Design Operating Characteristics in Clinical Trials with the Ordinal Scale Disease Progression Endpoint

Bayesian adaptive designs have gained popularity in all phases of clinical trials in the recent years. The COVID-19 pandemic, however, has brought these designs to the centre stage. The need for establishing evidence for the effectiveness of vaccines, therapeutic treatments and policies that could resolve or control the crisis has resulted in development of efficient designs for clinical trials that can be concluded with smaller sample sizes in a shorter time period. Design of Bayesian adaptive trials, however, requires extensive simulation studies that is considered a disadvantage in time-sensitive settings such as the pandemic. In this paper, we propose a set of methods for efficient estimation and uncertainty quantification for the design operating characteristics of Bayesian adaptive trials. The proposed approach is tailored to address design of clinical trials with the ordinal disease progression scale endpoint but can be used generally in the clinical trials context where design operating characteristics cannot be obtained analytically.