Selecting Promising Treatments in Randomized Phase II Cancer Trials with an Active Control

Abstract

The primary objective of phase II cancer trials is to evaluate the potential efficacy of a new regimen in terms of its antitumor activity in a given type of cancer. Due to advances in oncology therapeutics and heterogeneity in the patient population, such evaluation can be interpreted objectively only in the presence of a prospective control group of an active standard treatment. This paper deals with the design problem of phase II selection trials in which several experimental regimens are compared to an active control, with an objective to identify an experimental arm that is more effective than the control, or to declare futility if no such treatment exists. Conducting a multi-arm randomized selection trial is a useful strategy to prioritize experimental treatments for further testing when many candidates are available, but the sample size required in such a trial with an active control could raise feasibility concern. In this paper, we extend the sequential probability ratio test for normal observations to the multi-arm selection setting. The proposed methods, allowing frequent interim monitoring, offer high likelihood of early trial termination, and as such enhance enrollment feasibility. The termination and selection criteria have closed form solutions, and are easy to compute with respect to any given set of error constraints. The proposed methods are applied to design a selection trial in which combinations of sorafenib and erlotinib are compared to a control group in patients with non-smallcell lung cancer using a continuous endpoint of change in tumor size. The operating characteristics of the proposed methods are compared to that of a single-stage design via simulations: the sample size requirement is reduced substantially and is feasible at an early stage of drug development.

Sample size determination and treatment screening in two‐stage phase II clinical trials via ROC curve

“…Remark As the situations discussed in Kimani et al, Thall et al, Bischoff and Miller, and Cheung, we could not show the sample size reduction or the success rate increment of our method either. However, our method can be used to evaluate the quality for 2‐stage design with multiple treatments via the AUC of an ROC curve and provides the optimal conditional power for 2‐stage design approaches under the required sample sizes and critical values for interim and final stages.…”

“…For scenario 1, after determining n 1 , n 2 , u s1 , and u S under the constraints of overall type I and II error rates stated in (7) and (8), the expected sample size is as follows:…”

“…2. Determine u S1 and u S for each n and each n 1 whose range is in (5, n) under the overall type I and II error rate given in (7) and (8). Note that we restrict the sample size of each treatment to being greater than at least 5 for both stages.…”

“…Among them, Simon proposed the optimal 2‐stage design, for single arm trials, that minimizes the expected sample size for the binary endpoint cases, while Tsou et al were interested in the continuous endpoint scenarios. In addition, there are many others who investigated the requirement for phase II with multiple treatments …”

“…In addition, there are many others who investigated the requirement for phase II with multiple treatments. [4][5][6][7][8] In the interim analysis of a 2-stage design trial, we need to make a GO/NO-GO decision, and the conditional power (CP) is commonly used for the interim analysis, where CP is defined as the conditional probability of the event that the final result exceeds a critical value, given the current observed data information. Lachin 9 provided a systematic review of methods for futility stopping using the conditional power.…”