Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control

Abstract

In situations when many regimens are possible candidates for a large phase III study, but too few resources are available to evaluate each relative to the standard, conducting a multi-armed randomized selection trial is a useful strategy to remove inferior treatments from further consideration. When the study has a relatively quick endpoint such as an imaging-based lesion volume change in acute stroke patients, frequent interim monitoring of the trial is ethically and practically appealing to clinicians. In this article, I propose a class of sequential selection boundaries for multi-armed clinical trials, in which the objective is to select a treatment with a clinically significant improvement upon the control group, or to declare futility if no such treatment exists. The proposed boundaries are easy to implement in a blinded fashion, and can be applied on a flexible monitoring schedule in terms of calendar time. Design calibration with respect to prespecified levels of confidence is simple, and can be accomplished when the response rate of the control group is known only up to an interval. One of the proposed methods is applied to redesign a selection trial with an imaging endpoint in acute stroke patients, and is compared to an optimal two-stage design via simulations: The proposed method imposes smaller sample size on average than the two-stage design; this advantage is substantial when there is in fact a superior treatment to the control group.

Continuous Evaluation of Evolving Behavioral Intervention Technologies

“…In a Bayesian inference context, the randomization probability of a given BIT can be computed as the posterior probability that this given BIT is superior to the others, 25 although non-Bayesian formulation is also available (e.g., randomized play-the-winner 26 and sequential elimination). 17,27 This approach can also incorporate patient covariates, including preference, in the randomization, 28 to address patient heterogeneity.…”

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

“…In situations where ranking the experimental regimens is of interest, the elimination procedure may be applied without condition 2(c). As explained in Cheung (2008), applying condition 2(c) does not inflate the type I error rate, but may decrease P 1 by selecting a suboptimal treatment under H 1 more often. However, the impact has been found practically negligible via extensive simulations.…”

“…In this regard, a two-stage design lessens the sample size requirement when nothing works. On the other hand, as pointed out by Cheung (2008), the sample size advantage of a two-stage design disappears if there is an effective treatment among the experimental arms. In this paper, we study sequential selection boundaries that allow frequent interim looks and early stopping due to either selection or futility.…”

A hybrid geometric phase II/III clinical trial design based on treatment failure time and toxicity

“…Schaid, Wieand and Therneau (1990) proposed a similar 2-stage design for time-to-event outcomes that allows more than one E j to move forward to stage 2, allows termination of the trial in stage 1 for either futility or superiority, and does pairwise comparisons with the possibility of concluding that more than one E j provides an improvement over S . Many extensions of these designs have been proposed, including designs with more than two stages (Stallard and Todd, 2003; Stallard and Friede, 2008), a design that continues accrual between stages and uses the stage 1 data to determine the stage 2 sample size adaptively (Lin and Pledger, 2005), and an algorithm for computing decision boundaries (Cheung, 2008). …”