A group-sequential randomized trial design utilizing supplemental trial data.

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

M&M: A maximum duration design with the Maxcombo test for a group sequential trial of an immunotherapy with a random delayed treatment effect.

A random delayed treatment effect is expected in a confirmatory clinical trial for an immunotherapy due to the individual heterogeneity of physiological conditions. For this reason, the delay time will be assumed to follow a continuous distribution that is difficult to estimate accurately based on the early-phase data, which hinders the specification of the most powerful weighted log-rank test. Therefore, we propose a simulation-based maximum duration design with a robustly powerful Maxcombo test for a group sequential trial for the immunotherapy with the random delayed treatment effect. The design obtains the group sequential boundaries by a simulation procedure and determines the required maximum sample size using a one-dimensional search in which another simulation procedure is used to calculate empirical power. The simulation researches proved the accuracy of the group sequential boundaries and their robustness against the misspecified maximum sample sizes for large samples and revealed their moderate sensitivity against the misspecified survival distributions under the null hypothesis of no difference. The studies investigated whether the type I error rate would inflate under the "inferior" null hypothesis and evaluated the robustness against different distributions of the delay time in terms of the empirical power among the Maxcombo tests and component weighted log-rank tests.

Survival trial design and monitoring using historical controls.

In this paper, we propose a multistage group sequential procedure to design survival trials using historical controls. The formula for the number of events required for historical control trial designs is derived. Furthermore, a transformed information time is proposed for trial monitoring. An example is given to illustrate the application of the proposed methods to survival trial designs using historical controls. Copyright © 2016 John Wiley & Sons, Ltd.

Group Sequential Design for Randomized Phase III Trials under the Weibull Model.

In this article, a parametric sequential test is proposed under the Weibull model. The proposed test is asymptotically normal with an independent increment structure. The sample size for a fixed sample test is derived for the purpose of group sequential trial design. In addition, a multi-stage group sequential procedure is given under the Weibull model by applying the Brownian motion property of the test statistic and sequential conditional probability ratio test methodology.

Increasing the efficiency of clinical trials in neurodegenerative disorders using group sequential trial designs.

OBJECTIVES: Clinical trials in neurodegenerative disorders are facing high futility rates and rising development costs. We aim to review and exemplify the value of group sequential trial designs (i.e., designs with one or more prospectively planned interim analyses) within the field of amyotrophic lateral sclerosis. STUDY DESIGN AND SETTING: We reviewed the literature to identify sequentially conducted trials. Subsequently, we reanalyzed the dexpramipexole trial (EMPOWER), a classically designed and conducted trial involving 942 participants, by sequentially monitoring the functional questionnaire and survival endpoint. Finally, we simulated the performance of the sequential methodology under different treatment effects. RESULTS: Only six (12%) randomized, placebo-controlled trials incorporated stopping rules for both futility and superiority. Despite its high enrollment rate, sequential reanalysis of the EMPOWER study reduced the total trial duration with 140 days (23.4%, 95% confidence interval [CI] 13.2-34.4%), the number of follow-ups with 2,688 visits (23.6%, 95% CI 11.3-38.6%), and the total drug exposure time with 73,377 days (20.6%, 95% CI 9.8-35.9%). The functional questionnaire considerably increased the heterogeneity in the test statistics, which may negatively affect sequential monitoring. CONCLUSION: Group sequential trials can result in important reductions in the trial duration, which could make clinical trials more ethical by reducing the patients' exposure to noneffective treatments or by limiting their time on placebo.

Group Sequential Survival Trial Design and Monitoring Using the Log-Rank Test.

For randomized group sequential survival trial designs with unbalanced treatment allocation, the widely used Schoenfeld formula is inaccurate, and the commonly used information time as the ratio of number of events at interim look to the number of events at the end of trial can be biased. In this paper, a sample size formula for the two-sample log-rank test under the proportional hazards model is proposed that provides more accurate sample size calculation for unbalanced survival trial designs. Furthermore, a new information time is introduced for the sequential survival trials such that the new information time is more accurate than the traditional information time when the allocation of enrollments is unbalanced in groups. Finally, we demonstrate the monitoring process using the sequential conditional probability ratio test and compare it with two other well known group sequential procedures. An example is given to illustrate unbalanced survival trial design using available software.