Cytel Blog

Early stage Phase 2 clinical trials are often designed as multi-stage single arm trials, which quickly identify inefficacious molecules and interventions, without subjecting too many patients to treatments with questionable standard of care. As the primary purpose of these designs is the early stopping for futility, it is often the case that very small cohorts enroll in early stages of the design. A larger cohort is only allowed to enroll when results from earlier enrollment suggest that there is clinical benefit to the new treatment.

The rise of Bayesian methods has meant that predictive power can be used to assess efficacy during these single arm Phase 2 studies, but how do they differ from traditional designs and when should they be used?

In the world of clinical trials, the pace of innovation is accelerating, and approaches such as Bayesian methods are gaining traction. These methods bring flexibility and speed to clinical trial design and analysis, and with increased access to the necessary computational power, are transforming today’s clinical research ^[1]. However, the number of simulation and modeling tools necessary to perform Bayesian computations requires statisticians to be well-resourced technologically. Many biostatisticians may not readily have access to the cloud computing power to make these design approaches practical within the time constraints afforded for statistical design.

There are many dose-finding designs that have been developed over the past 30 years and several more are anticipated ^[2]. Sponsors often face the dilemma of choosing from the various design options available today. Finding the right dose in Phase 2 gives a potential new therapy its best chance to demonstrate efficacy during Phase 3, and Bayesian techniques prove to be useful for optimal dose-finding.

The convergence of several distinct trends has made wearables an increasingly attractive option for use in confirmatory clinical trials. A number of considerations arise, though, when sponsors choose this route, from how to construct clinically meaningful digital biomarkers, to how to determine the quality of the data they collect.

A recent Cytel webinar illustrated how wearables have been used in Parkinson’s disease, as well as in studies where actigraphy became a vital endpoint. Here are three considerations for utilizing wearables in clinical studies that emerged during this study.

C-Suite and R&D Decision-Makers are always striving to make evidence-driven decisions. Yet the rules by which evidence is evaluated can bias these decisions, even when the method of decision-making seems objective. Our Chief Scientific Officer, Dr. Yannis Jemiai, has worked extensively on how to operationalize decision theoretic tools for clinical development decision-making. Here he introduces three quantitative frameworks that life-sciences decision-makers can quickly incorporate into their selection process when selecting an optimal design for their next clinical trial.

In the last few years, there has been a growing interest in historical borrowing or augmented trials. There is an increasing level of comfort in using these methodologies even in confirmatory trials setting. The key challenge in borrowing external information is the selection of appropriate historical studies or external data sources. There are benefits to historical borrowing but also potential risks (for example, Type I error and power can be impacted by the drift).

However, despite the risks, several projects submitted to the FDA’s Complex Innovative Designs (CID) initiative aim at using historical controls in Phase III studies. Many data-sharing initiatives such as, TransCelerate, Project Datasphere and others, are all working towards making clinical trial data available for repurposing and reuse across the industry. There are also several working groups such as, the European EFSPI/PSI Historical Data Special Interest Group and DIA Bayesian Working Group who are interested in this area. This blog aims to introduce the concepts of evidence synthesis and Bayesian dynamic borrowing.

The COVID-19 Pandemic prompted the rapid surge in the generation of clinical data that has been scattered across multiple platforms, making it challenging to measure comparative treatment effects across trials. Last year, Cytel launched a COVID-19 Trial Tracker, an Open Access tool to track the global response to the pandemic. We talk to Louis Dron, Director - Real World Analytics at Cytel, about the evolution of Cytel’s Trial Tracker and the vision for its future developments.

The past two years have witnessed a heightened interest in the use of wearables in clinical development. The unexpected changes to the industry ushered in by the COVID-19 pandemic has highlighted the need for remote monitoring and patient-centric outcomes and accelerated the changes in the trials conduct.

Below we identify six elements critical to integrating wearables into your clinical development program.

Recent years have witnessed improving survival outcomes for those struggling with a range of common kidney cancers. Scientists at Cytel recently published findings aiming to identify those baseline factors which influence a positive response to an established therapy. Such an investigation is critical to ensure that future treatment is informed by biomarker driven strategy.

The past decade has witnessed the rise of simulations-based clinical trial optimization in a manner unimaginable to most only a few years ago. Such optimization has become an integral aspect of strategic clinical trial design. The initial techniques of operationalizing Monte Carlo methods within a study design setting have increased and transformed in the landscape of cloud-powered computing. Nowadays, technology can produce innumerable simulations within a short space of time. Cytel’s Solara, for example, recently ran 1.5 million simulations within a fifteen minute period to identify trial designs optimized over speed, trial cost and probability of success. Why is it then, that some trial sponsors still struggle to make use of such simulations?