  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.
