The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Since 1953, when the discovery of the structure of DNA was made, we have seen great advancements in genomics. Particularly, in the last few years, the industry has seen a rapid rate of adoption in biomarkers and how they can be used to improve biomedical interventions. Trial investigators have been showing interest in biomarker-guided trials such as basket trials and umbrella trials, developed under the master protocol framework. As a result, we have been seeing a rapid rate of adoption of these innovative trial methods.
In our previous blog, we spoke with Jay Park, Director, Cytel, about the concept of master protocols, their importance and future growth potential. On March 19, Cytel conducted a webinar with Jay on “Key Design Considerations for Basket Trials and Umbrella Trials”. This webinar introduced two master protocol types and explored their extension to design in various contexts from the HIV epidemic in global health to expedited oncology trials. Continue reading for key highlights from the webinar .
Register now to get free access to webinar slides and recording.
In September 2018, the FDA provided a draft guidance on master protocols reflecting an increased interest in these designs by industry. This came after a 2017 editorial published by the Drs. Woodcock and LaVange from the FDA in the New England Journal of Medicine. In this guidance master protocol is defined as a protocol designed with multiple substudies, which may have different objectives and involves coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure. As the adoption of these innovative trial methods is on the rise, we speak with Jay Park, Director, Cytel, about the concept of master protocols, their importance and future growth potential. We take a closer look at their use in oncology trials where the increase in biomarker driven enrichment and stratification designs, as well as the use of companion diagnostics, ensures that master protocols are often adopted by trial investigators.
The Cytel Trial Design Innovations (CTDI) Webinar Series recently hosted a webinar on designing event-based studies. Such studies are essential to designing high-efficiency clinical trials in certain therapeutic areas, but they add a number of challenges to the already complex landscape of adaptive trials.
The webinar was held on January 23rd, featuring Biostatistician and pioneering Bayesian trial-designer Pantelis Vlachos. We had the opportunity to sit down with Dr. Vlachos and speak about innovative trial designs and their benefits, adaptations and interim looks in oncology and cardiovascular, the challenges of designing event-based studies more generally, and how Cytel’s array of software tools, particularly East®, has enabled trial sponsors to fully consider their options in the design of high-efficiency clinical trials.
January’s Cytel Trial Design Innovations (CTDI) Webinar Series will feature Biostatistician and pioneering Bayesian trial-designer Pantelis Vlachos. Next week, Dr. Vlachos will speak on high-efficiency trial design for Event-Based Studies, particularly in oncology and cardiovascular trials. In this blog post, we offer a glimpse of Dr. Vlachos’ last CTDI Webinar (February 2019) on high-efficiency trial design using enrichment strategies.
Don't miss next week's webinar "Designing Event-based Studies: Reduce Sample Size and Increase Predictability"! Click on the button below to register.
Interview with Kannan Natarajan: Drug Development in Rare Diseases - Need for Innovation in Statistical Thinking
Cytel is delighted to have Kannan Natarajan speaking at the “Complex Innovative Trial Design Symposium and East User Training” on November 6 in Boston, MA. We got a chance to sit down with Kannan and talk about his career in statistics, the changing role of statisticians, his views on evolving statistical thinking, estimands and relevance of technology in the context of rare diseases.
A disease is generally considered to be rare if it affects one patient per 200,000 people (1) and most rare diseases affect far fewer than this. However, collectively rare diseases are relatively common, affecting 350 million patients worldwide (2). The path to diagnosis for these patients is often a long, difficult battle and even once the diagnosis is made, it is likely there will be no suitable treatment available. For 90% of rare diseases, there is no approved therapy (2). There is, therefore, a pressing need to develop new, effective therapies that can bring hope to rare disease patients. However, the clinical development environment for life-threatening, rare diseases is fraught with challenges. By their very nature, rare indications have few patients and limited sample size. This scarcity of patients also results in a lack of available information and knowledge about the disease from the best endpoints, to the treatment effect size or the variability of response between subgroups.
The term biomarker signature describes the behavior of a set of biomarkers that define a signature to maximize the prediction performance. We examine the behavior of specific biomarkers as a set that consistently fluctuate together to maximize the accuracy on predicting the disease-related outcome.
How we apply a biomarker signature depends on the prediction problem. A prognostic biomarker signature is used to predict the disease progression, a risk biomarker signature is used to identify sets of subjects that are likely to develop a disease, and a predictive biomarker signature is used to determine the patients that are likely to respond to a particular treatment. Predictive biomarker signatures are used often in oncology to stratify patients with a specific cancer into sub-populations and develop targeted therapies for the diseased population subtypes defined by the biomarker signature.
In this blog, we share an example project that our data science team has worked on supporting this work. The case study forms part of a new ebook 'Innovative Data Science and Real-World Analytics Approaches in Practice' and we are also delighted to provide the link for download as part of the article.
By Nicolas Rouillé and Eric Henniger
The right design and the right data ultimately leads to the right decisions, so obtaining fit-for-purpose data, collected based on what your protocol is looking for is vital. However, there are several data pressure points facing oncology drug developers that need specialized expertise and processes to handle. In this blog, we run through some key aspects to consider to smooth your data collection and analysis.
At the Partnerships in Clinical Trials Conference in Barcelona in November 2018, Strategic Consultant Ursula Garczarek participated in a thought leadership stream tackling various developments in Real World Evidence, Pediatrics, Trial Design & Big Data. As well as participating in a panel discussion on innovations in clinical trial design, she was impressed by the contributions and innovations offered from other stakeholders across the healthcare landscape. One of these exceptional contributions came from from Galina Velikova, Professor of Psycho-social and Medical Oncology at the University of Leeds, and in this blog, we are thrilled to share an interview with Professor Velikova, in which she expands her discussion of the role of Patient Reported Outcomes (PROs) and Quality of Life Measures in trials and clinical practice.