Bayesian Simultaneous Credible Bands for Polynomial Regression

Abstract

Quantifying efficacy uncertainty across the entire dose range is crucial in dose-response studies. Although the frequentist simultaneous confidence band (FSCB) is widely used for this purpose, it does not readily incorporate prior knowledge. The Bayesian simultaneous credible band (BSCB) offers a natural alternative, yet practical methods for constructing BSCBs remain scarce in the literature. In this paper, we propose a unified framework for constructing a BSCB for the regression curve in a univariate polynomial model over a finite covariate interval. An efficient simulation-based procedure is developed to determine the critical constant of a BSCB. The framework accommodates inference under different levels of prior information and can be implemented either analytically or via posterior sampling methods. Notably, we prove that under mild regularity conditions, the BSCB is asymptotically equivalent to the FSCB, thereby attaining the nominal frequentist coverage for a broad class of priors. Simulation studies confirm that the BSCB attains the exact posterior simultaneous coverage probability across various scenarios. An application to a dose-response study illustrates its importance in identifying the minimum effective dose in Phase II clinical trials. Software implementation of the proposed methods is available in an accompanying R package.