In clinical trials, the use of sequential stopping rules or stopping boundaries has been advocated from both frequentist and Bayesian perspectives. Elsewhere, we have shown that a large family of untruncated stopping boundaries that are of interest from a frequentist point of view are also Bayes optimal boundaries. In this paper we examine a specific class of truncated rules, focusing especially on those related to work of (1). We show that it is possible to choose a cost and loss structure such that stopping boundaries of the type discussed by Lan and DeMets are also optimal Bayesian boundaries.
