Bayesian dose selection design for a binary outcome using restricted response adaptive randomization

Over-accrual in Bayesian adaptive trials with continuous futility stopping

BACKGROUND

We explore frequentist operating characteristics of a Bayesian adaptive design that allows continuous early stopping for futility. In particular, we focus on the power versus sample size relationship when more patients are accrued than originally planned.

METHODS

We consider the case of a phase II single-arm study and a Bayesian phase II outcome-adaptive randomization design. For the former, analytical calculations are possible; for the latter, simulations are conducted.

RESULTS

Results for both cases show a decrease in power with an increasing sample size. It appears that this effect is due to the increasing cumulative probability of incorrectly stopping for futility.

CONCLUSION

The increase in cumulative probability of incorrectly stopping for futility is related to the continuous nature of the early stopping, which increases the number of interim analyses with accrual. The issue can be addressed by, for instance, delaying the start of testing for futility, reducing the number of futility tests to be performed or by setting stricter criteria for concluding futility.

How cutting-edge trial design can assess outcomes

PURPOSE OF REVIEW

Randomized clinical trials (RCTs) have come to be accepted as the gold standard for assessing the efficacy and effectiveness of therapeutics and interventions in medicine. In this paper, we aim to describe some evolving concepts associated with the design and conduct of RCTs and outline new approaches aiming to increase efficiency and reduce costs.

RECENT FINDINGS

A well-powered and performed RCT is usually a study involving several different centers from different geographical areas that enrolls a large number of patients in diverse clinical settings. Altogether, these features increase the generalizability of the study and make the rapid implementation of the findings more likely. However, this does not come without cost. Among several possible alternatives to conventional RCTs, the most important ones are related to the unit of randomization (individual vs. cluster), study design (conventional vs. adaptive), randomization scheme (fixed vs. response-adaptive), data collection (conventional case report forms vs. registry-embedded) and statistical approach (frequentist vs. Bayesian).

SUMMARY

While conventional RCTs remain the gold standard for generating evidence, new trial designs may be considered to reduce sample size and costs while improving trial efficiency and power. However, they raise new challenges for testing feasibility, conduct, ethical oversight and statistical analysis.

Novel Clinical Trial Designs to Improve the Efficiency of Research

SUMMARY

Large randomized trials provide the highest level of clinical evidence. However, enrolling large numbers of randomized patients across numerous study sites is expensive and often takes years. There will never be enough conventional clinical trials to address the important questions in medicine. Efficient alternatives to conventional randomized trials that preserve protections against bias and confounding are thus of considerable interest. A common feature of novel trial designs is that they are pragmatic and facilitate enrollment of large numbers of patients at modest cost. This article presents trial designs including cluster designs, real-time automated enrollment, and practitioner-preference approaches. Then various adaptive designs that improve trial efficiency are presented. And finally, the article discusses the advantages of embedding randomized trials within registries.

Comparison of methods for control allocation in multiple arm studies using response adaptive randomization

BACKGROUND/AIMS

Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting.

METHODS

We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial.

RESULTS

The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation.

CONCLUSION

Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials

Limited resources are a challenge when planning comparative effectiveness studies of multiple promising treatments, often prompting study planners to reduce the sample size to meet the financial constraints. The practical solution is often to increase the efficiency of this sample size by selecting a pair of treatments among the pool of promising treatments before the clinical trial begins. The problem with this approach is that the investigator may inadvertently leave out the most beneficial treatment. This paper demonstrates a possible solution to this problem by using Bayesian adaptive designs. We use a planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis as an example of the approach. Rather than having to guess at the two best treatments to compare based on limited data, we suggest putting more arms in the trial and letting response adaptive randomization (RAR) determine better arms. To ground this study relative to previous literature we first compare RAR, adaptive equal randomization (ER), arm(s) dropping, and a fixed design. Given the goals of this trial we demonstrate that we may avoid 'type III errors' - inadvertently leaving out the best treatment - with little loss in power compared to a two-arm design, even when choosing the correct two arms for the two-armed design. There are appreciable gains in power when the two arms are prescreened at random.