A Bayesian group sequential schema for ordinal endpoints

Abstract

The ordinal endpoint is prevalent in clinical studies. For example, for the COVID-19, the most common endpoint used was 7-point ordinal scales. Another example is in phase II cancer studies, efficacy is often assessed as an ordinal variable based on a level of response of solid tumors with four categories: complete response, partial response, stable disease, and progression, though often a dichotomized approach is used in practices. However, there lack of designs for the ordinal endpoint despite Whitehead et al. (1993, 2017), Jaki et al. (2003) to list a few. In this paper, we propose a generic group sequential schema based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. We conducted extensive simulations to demonstrate the desirable performances of the proposed method and an R package BayesOrdDesign has also been developed.