Cytel Blog

2020 has been an unusually difficult year as the global pandemic impacted all of our lives. This year, the Cytel blog saw a lot of activity as we tried to keep our readers abreast with the latest updates on the COVID-19 clinical trials, and covered other trending and important topics such as, the growing adoption of Synthetic Control Arms, master protocols, Head to Head Comparisons and Bayesian methods. We also collaborated with several experts from both within and outside the company to conduct several series of webinars and provided summaries through our blogs.

Continue reading to learn about the top 10 Cytel blogs that resonated most with our community in 2020.

Can I submit software programs other than SAS? What software programs should I submit? Are sponsors required to submit executable programs?

Do I need to rename my software programs so that they all have the same extension e.g. “.txt”?

Can I make use of macros in my software programs and if so, should macros be part of the submission package?

What kind of documentations for software programs should I include in the submission package?

Do I need to follow any particular style and conventions when writing software programs that will be part of a submission package?

A single topic generates so many questions! Get the answers in this blog.

An extraordinary amount of global research is underway as the COVID-19 pandemic continues to evolve and spread. As several entities develop curative and preventive responses against COVID-19, alignment with regulatory recommendations is key for developing effective and safe intervention. Moreover, fast regulatory approval will translate into early availability of interventions to address unmet needs.

Continue reading to get an overview of the registered COVID-19 clinical trials landscape, with a story on the special attention received by Hydroxychloroquine treatment.

 

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.

The combination of greater access to electronic health records, bigger electronic claims datasets, and the need for more clinical insight in ensuring patient safety, has made observational studies an important new tool in trial design. Observational studies typically take non-randomized data from outside of a trial and use quantitative and modeling techniques to draw conclusions from big datasets. While typically used for HEOR and market access, augmenting regulatory submissions with observational studies is gaining prominence. As with all data analyses, there is an implicit rule of ‘garbage in-garbage out,’ where data that is not up to the standard required for the formation of sound scientific judgment, should not be used. Sponsors should rely on the most sophisticated tools and advanced analytics to make the most rigorous use of available data.

 

Cytel and Novartis are together hosting a complimentary Bayesian Virtual Symposium and an Interactive 7-part workshop. This series will expose you to cutting edge topics from industry renowned leaders in Bayesian statistics. The introductory webinar “Bayesian Statistics and FDA Regulatory Acceptability” is presented by Greg Campbell, PhD, Former Director of Biostatistics, U.S. Food and Drug Administration.

In the United States Bayesian statistics has been used in regulatory submissions to the Food and Drug Administration (FDA) for confirmatory clinical trials medical devices for more than fifteen years. In this webinar, Dr. Campbell reviews the Bayesian history and accomplishments for medical devices. He talks about the status and opportunities of Bayesian statistics for pharmaceutical drugs and biologicals. We also learn about the challenges and the future of Bayesian statistics in the regulatory environment. You can access the on demand webinar and register for the rest of the series by clicking the button.

“A good start is half the battle” (the Before) when submitting data to the FDA and there are a couple of cherries to put on top (the After) when your regulatory group has finally submitted the eCTD to the FDA^[1] . A good start is to have early discussions with the agency by regularly meeting them and sharing the status of your clinical data standards. While, the cherry on the top is the continuous support you need to guarantee to your submission project to promptly react when the reviewers come back with questions and additional requests during the review process.

On Friday September 11, Cyrus Mehta, co-founder of Cytel, will be delivering a talk to the Heart Failure Collaboratory, on how adaptive designs can be utilized to salvage trials disrupted by COVID-19. The Heart Failure Collaboratory is a public-private consortium with FDA, and will be hosting a day long symposium online, on the application of innovative methods for drug and device studies in the age of COVID-19.

CDISC standards have been around for a while with the first SDTM Standard version released in 2004. However, it was only in the last decade that it became “The Standard”, particularly when Health Authorities (HA), such as the US FDA and Japanese PMDA, made it a requirement for data submissions to support most of the regulatory requests for market approval. Additionally, most of the Pharma companies made the CDISC standards a part of their operational data model and consequently, the number of studies using the CDISC standards increased across phases of development.

The benefit of receiving data in standard formats was soon recognized by HA reviewers as they now require lesser time to understand the structure of the data they receive. Integration of data provided by different sponsors, for example on the same indication, for better understanding of safety signals, has become possible with data submitted in standard CDISC format.

However, the HAs such as the US FDA, soon realized that this was not enough, for two main reasons:

1. sponsors sometimes make bad or different interpretations of the standard
2. lack of standards or use cases in specific disease areas or indication

The first issue has been addressed by a number of specific HA conformance technical guidance ^[1,2] and regular public webinars where the HAs presented to the Industry real examples of bad standards implementations ^[3]. For the second issue, it was soon clear that the gap to be fixed was bigger and required more investment and resources. The CDISC Therapeutic Area User Guidance (TAUGs) are an “attempt” to fill this gap and reduce interpretation, and misinterpretation, of the standards ^[4].