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We define and compare several methods for specifying early stopping boundaries as functions of interim sample size, rather than as fixed cut\u2010offs, using Bayesian posterior probabilities as decision criteria. We consider boundaries with constant, linear, or exponential shapes. For design optimization criteria, we use the TPP and mean number of patients enrolled in the trial. Simulations to evaluate and compare the designs' operating characteristics under a range of scenarios show that, while there is no uniformly optimal boundary, an appropriately calibrated exponential shape maintains high TPP while limiting the number of patients assigned to a treatment with an inferior response rate or an excessive toxicity rate.<\/jats:p>","DOI":"10.1002\/pst.2046","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T05:09:48Z","timestamp":1595912988000},"page":"928-939","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Comparing Bayesian early stopping boundaries for phase <scp>II<\/scp> clinical trials"],"prefix":"10.1002","volume":"19","author":[{"given":"Liyun","family":"Jiang","sequence":"first","affiliation":[{"name":"Research Center of Biostatistics and Computational Pharmacy China Pharmaceutical University  Nanjing China"},{"name":"Department of Biostatistics 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