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?

Most people know that clinical drug discovery is usually conducted using either Frequentist or Bayesian methods. These two statistical paradigms have enjoyed a degree of competition historically, with some statisticians tauting the statistical rigor of Frequentist designs and others the intuitiveness and flexibility of Bayesian clinical trials. Recently, though, a number of hybrid methods have arisen, that leverage the benefits of both paradigms for singularly powerful clinical trials. A new Cytel article outlines the benefits of these combined methods.

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.

In a previous post, I discussed the importance of proper use of CDISC Controlled Terminology (CDISC CT) in SDTM. However, the CDISC-CT is not the only submission terminology you need to be familiar with when building SDTM datasets to be submitted to the FDA (and similarly to the PMDA). As per the FDA Data Standards Catalog, when submitting datasets to the agency, you need to follow not only the CDISC standards (SDTM, ADAM, define-xml and CDISC-CT) but also a number of other submission terminologies. For example, this is the case of MedDRA when your SDTM package contains Adverse Events data, or WHO Drug Dictionary for Medications, but there are also a number of other submission terminologies you need to apply, particularly in the TS – Trial Summary Dataset.

A number of presentations and papers have been published discussing TS domain^[1] and clarify requirements that are not always fully clear in the SDTM IG or in the agencies Technical Conformance Guide.

In this blog, I focus on TS and discuss some specific parameters that you need to submit in TS using various “external” dictionaries, and help you understand how to find the correct term (and code).

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.

In recent times, Single arm trials are being increasingly used to assess new treatment interventions. They establish clinical benefit by demonstrating the effects of a new therapy or treatment, without the need to use placebo or standard of care as a control. Instead, an alternative approach known as external controls or synthetic control arms (SCA) are being used that leverages real world data and historical datasets. Technical knowledge of Bayesian methods is key to being able to design and implement such trials.

Breakthrough treatments in oncology and rare diseases are now commonly approved based on a pivotal single arm trial – however this is not always optimal. Use of single arm trials in oncology or rare diseases requires appropriate comparisons to be developed to document the benefits of the new treatment. Deriving such comparisons from real world or historical trial data is not straightforward and requires data source and methods expertise.