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In phase II clinical trials, patients are recruited sequentially and consequently the time required to complete the clinical trial will become long if the accrual rate is low. To speed up the drug development process and account for ethical issues, stochastically and non-stochastically curtailed two-stage designs have been proposed in single-arm phase II clinical trials. More recently, randomized phase II clinical trials are being increasingly recommended to avoid biased evaluation of the treatment effect when compared with a historical control. The current patient population and the historical one may be quite heterogeneous. Moreover, it is impossible to randomly assign patients for treatments. Consequently, various two-stage designs have been presented for comparing two arms. Since the sample size required in a randomized phase II trial is usually larger than that required in a single-arm phase II trial, we introduce the concept of curtailed sampling procedure to develop curtailed two-stage design for two-armed, randomized phase II clinical trials. The proposed design does not require pairwise patient response comparison, yet it allows a trial to be stopped early as soon as the difference in therapeutic effect of the experimental therapy and the standard at the end of a trial is foreknown., (Copyright © 2017 Taylor & Francis Group, LLC.)

In clinical research, it is not uncommon to modify a trial procedure and/or statistical methods of ongoing clinical trials through protocol amendments. A major modification to the study protocol could result in a shift in target patient population. In addition, frequent and significant modifications could lead to a totally different study that is unable to address the medical questions that the original study intended to answer. In this article, we propose a logistic regression model for statistical inference based on a binary study endpoint for trials with protocol amendments. Under the proposed method, sample size adjustment is also derived.

In clinical development, an adaptive design combining results from two separate studies (e.g., a seamless adaptive design with a dose finding study phase and a confirmatory study phase) is commonly considered. The purpose of an adaptive design is not only to reduce lead time between the two studies, but also to evaluate the treatment effect in a more efficient way. In this paper, the focus is on the case where the study objectives are the same but the time durations of the study periods are different in the two stages. In particular, event data are collected in both stages. Statistical procedure for combining data observed from the two different stages is discussed. Furthermore, results on hypotheses testing and sample size calculation are derived for the comparison of two treatments.