The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials

Abstract

Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.

Keywords: Bayes factor; local prior; nonlocal prior; phase II; sequential monitoring.

Figure 1:

Example of the iMOM prior restricted in the range of (0.2, 1] with k = 1, ν = 2, and τ = 0.06.

Figure 2:

Operating characteristics of sequential monitoring using the Bayes factor and TS design under a weakly informative prior (ESS=5) and strongly informative prior (ESS=20).

Figure 3:

Probabilities of claiming superiority when H₁ is true (i.e., the treatment is considered efficacious), and probabilities of claiming futility when H₀ is true (i.e., the treatment is considered futile) at each interim time, and the final analysis when the maximum sample size is 50, given different values of prior ESS (5 versus 20). The numbers in parentheses represent the overall probabilities of claiming superiority (a1 & b1) or futility (a2 & b2) in the trial.