The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
As a part of Cytel’s "New Horizons Webinar Series", Alind Gupta, Senior Data Scientist, presents case studies from his research on applying machine learning for predictive analysis and evidence generation.
The biopharmaceutical and healthcare industries now collect more data than ever before due to advances in the variety of information sources combined with the ability to store vast quantities of diverse data. Sophisticated machine learning (ML) and AI techniques allow us to access and analyze any combination of a multitude of data sources. The way that traditional controlled sources are viewed is being adapted in light of new evidence that emerges from real-world data. In his presentation, Alind introduces us to the concept of ML and Causal Inference and discusses case studies from randomized clinical trials and real-world data.
Click the button to register for the on demand webinar.
TOGETHER trials, and the advantages of adaptive platform designs for investigating COVID-19 therapies
Cytel has recently designed and implemented the TOGETHER Trials, funded by the Bill & Melinda Gates Foundation to generate knowledge to help fight COVID-19, particularly in low and middle-income countries. The trials, with sites in Brazil and South Africa, test three existing interventions as possible treatments for COVID-19 in high-risk adults who do not require hospitalization, compared to a placebo.
The TOGETHER trials use an adaptive platform design. This type of design is particularly useful for contexts such as COVID-19 response, where there are many unknowns and a need for accelerated and resource-efficient answers, for 5 reasons.
Just as there are numerous adaptations that fall within the umbrella of adaptive designs, there are several different statistical methods that can lead to the construction of a synthetic control arm. Cytel’s ebook, “Demystifying synthetic control arms”, is an effort to explain common strategies for their construction. Download the ebook by clicking on the button.
In this blog, we talk with Robert Greene, Founder and President of the HungerNDThirst Foundation, about his upcoming presentation at Cytel’s East User Group Meeting on 14th and 15th November at Merck in Darmstadt, Germany. Robert will bring a fresh perspective to the discussion of the role statisticians can play in enhancing the position of patients in clinical trials. Patient-centricity is a key topic in modern drug development, and this session aims to encourage statisticians to question the importance of a more patient-centric approach within their field.
In this blog, we talk with Simon Kirby, former Senior Director at Pfizer, about his upcoming presentation at Cytel’s East User Group Meeting on 14th and 15th November at Merck Darmstadt, in Germany. Simon will address the topic of Selection Bias for Treatments with Positive Phase 2 Results and in this blog he explains why this is a key topic of particular relevance for pharmaceutical companies in today’s climate of accelerated development. He also talks with us about his career in statistics, current research, and his book Quantitative Decisions in Drug Development.
We are delighted that Stephen Senn will be joining us at the EUGM on November 14th and 15th in Darmstadt, Germany. In this blog, we sit down for a discussion with Stephen about his career in statistics, his advice for early career statisticians, his upcoming research, and the topic of his presentation at the East User Group Meeting “70 Years Old and Still Here: the Randomized Clinical Trial and its Critics”.
In the randomized clinical trial (RCT), the process of deciding the randomization method and implementing is critically important. Unfortunately, it is not unheard of for problems to arise. In an article (Downs et al 2010 1), it is noted that as well as initial errors of trial design, problems can arise from errors with programming of the randomization or even human error during the course of the trial. Maintaining the rigor of the RCT relies on robust and reliable randomization with no errors. If treatment allocation is inadequately concealed then overestimation of treatment effect can occur, and the ‘randomized’ control trial becomes effectively ‘non-randomized’ – putting the entire study at risk (2).