Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Blinded sample size re-estimation for comparing over-dispersed count data incorporating follow-up lengths

Blinded sample size re-estimation (BSSR) is an adaptive design to prevent the power reduction caused by misspecifications of the nuisance parameters in the sample size calculation of comparative clinical trials. However, conventional BSSR methods used for overdispersed count data may not recover the power as expected under the misspecification of the working variance function introduced by the specified analysis model. In this article, we propose a BSSR method that is robust to the misspecification of the working variance function. A weighted estimator of the dispersion parameter for the BSSR is derived, where the weights are introduced to incorporate the difference in the distribution of follow-up length between the interim analysis with BSSR and the final analysis. Simulation studies demonstrated the power of the proposed BSSR method was relatively stable under misspecifications of the working variance function. An application to a hypothetical randomized clinical trial of a treatment to reduce exacerbation rate in patients with chronic obstructive pulmonary disease is provided.

Blinded continuous monitoring in clinical trials with recurrent event endpoints

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.