Methods allowing unplanned adaptations to the sample size based on the interim estimate of treatment effect do not base inference on the minimal sufficient statistic and suffer losses in efficiency when compared to group sequential designs [1, 2, 3]. However, when adaptive sampling plans are completely pre-specified at the design stage of the trial, investigators can proceed with frequentist inference based on the minimal sufficient statistic at the analysis stage. In the context of two general settings where different optimality criteria govern the choice of clinical trial design, we quantify the relative costs and benefits of a variety of fixed sample, group sequential, and pre-specified adaptive designs with respect to standard operating characteristics. We find pre-specified symmetric adaptive designs that are ``optimal" in the sense that they minimize the expected sample size at the design alternatives. Our results build on others' prior research [1, 4, 5, 6] by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. These findings provide insight into what are good and bad choices of adaptive sampling plans and suggest that adaptive designs proposed in the literature are often based on inefficient rules for modifying the sample size.


Clinical Trials