The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Nowadays, it’s difficult to pick up a mainstream newspaper or read an industry publication without seeing reference to Artificial Intelligence or AI and progress towards innovations like autonomous vehicles, or customer behavior prediction. For the biopharma industries specifically, AI represents an opportunity to avert the R&D productivity crisis with paradigm-shifting applications such as in-silico drug design, prediction of trial risks and big data analytics.
However, with every opportunity, there are risks and challenges, and in this blog, I will discuss how pharma needs to address the opacity of AI to ensure trust and credibility with all stakeholders.
In honor of Rare Disease Day 2019 we share a new Cytel podcast featuring Cytel Strategic Consultant Ursula Garczarek discussing how innovative statistical approaches can overcome challenges in rare disease development. Below, you can access the podcast and a summary of some of Ursula's key insights from working in rare diseases and interacting with regulatory agencies for complex and innovative designs.
In 2018, Cytel ran a qualitative survey among biostatisticians and programmers on trends in data science and perceptions about the goals, barriers and future of the field in the biopharma and life science industry. Our analysis and report revealed a range of insights from the respondents including :
Lack of shared understanding of what data science represents with less than 1 in 7 of all respondents suggesting a definition of data science.
Clear trend of investment in data science across organizational types with three-quarters of all respondents saying their organizations had a dedicated data science department.
An opportunity for improved clinical trial design by using data science techniques was recognized by the majority of respondents. In addition, respondents across all functions perceive the key opportunity for data science to be in maximizing the value of real-world data.
In a recently published discussion on The Effective Statistician podcast ( a weekly podcast produced in association with PSI) Ursula Garczarek, Associate Director Strategic Consulting at Cytel sat down with hosts Alexander Schacht and Benjamin Piske to discuss where the biopharma and life science industries are headed with the application of data science.
Happy New Year! As we look ahead to future successes and the new advancements in drug development that 2019 will bring, we are taking a moment to reflect on the topics that resonated most with our community on the Cytel blog in 2018. While these 6 most popular blogs encompass a variety of topics from across the data science, statistics, and statistical programming space, they all have in common a focus on innovative practices and application of statistical, data management, and data science excellence to achieve better outcomes in drug development.
PhUSE EU Connect 2018 took place in Germany’s financial capital Frankfurt, 4th - 7th November and brought together a range of experts to tackle the most pressing issues facing statistical programmers today. The agenda was superb with 143 presentations in 16 different streams and nearly 30 posters. This year’s event theme ‘Future Forward’ did not disappoint and there were some very thought-provoking talks on the drug development industry's challenges and what we can do in the future to meet these challenges. Additional hot topics were: Analytical Risk Based Monitoring, Machine Learning, and Data Standards and Governance. We found this year's event informative and well attended.
In this blog, we share the contributed posters and presentations from our Statistical Programmers and summarize some of the particular highlights from the sessions and posters that our team members attended.
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 artificial intelligence (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. A recent Deloitte survey (1) found that 90 percent of biopharma companies are making significant investments in
real-world evidence capabilities to drive drug development and meet regulatory requirements.
Real-world evidence (RWE) has historically been used for post-marketing endorsement and in pricing and reimbursement negotiations. But could data science offer an opportunity to fundamentally shift this
paradigm, leading to better and more affordable medications being approved on the basis of RWE?
In June 2018, Cytel created and ran a survey asking respondents from our audience about the potential of data science approaches in the sector. We are now excited to share the insights from the survey* ( designed as a qualitative pulse check) which reveal a powerful potential shift in the current drug development and approval paradigm.
JSM 2018, ASA’s annual gathering of over 6500 attendees attracted statisticians and data scientists to the beautiful city of Vancouver on July 28 – August 2. The conference offers a one of a kind opportunity for statisticians to exchange ideas and explore opportunities for collaboration. In this blog, we will provide access to our team's slide decks from the event, as well as some of their key takeaways from sessions that they attended.
By Gordhan Bagri and Munshi Imran Hossain with H A S Shri Kishore
Shiny (from RStudio) is one of the most popular R packages. The package allows programmers to create applications with interactive user interfaces. These applications can then be deployed for non-programmers to perform analysis. Non-programmers can, therefore, make use of the statistical capabilities of R by means of point and click. This is one of the reasons why its use has been on the rise in the last few years.