Cytel Blog

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.

In our previous blog, “Remote Working Arrangement – How to get it right?”, we talked about how the need for social distancing has led most of the employers, across the globe, to make work-from-home arrangements for their employees. As we continue to stay indoors and combat COVID-19, keeping aside some time every day to read and watch useful resources on important industry topics can be very helpful. Cytel's team of oncology trial design and advanced analytics experts have been hosting a series of complimentary webinars covering a range of innovative topics including adaptive design, machine learning, estimands and trial design software. In this post, we offer you a recap of the webinars we conducted in the past few weeks. You can register for the upcoming webinars in our oncology series by clicking on the button below.

In our previous blog, we spoke with Alind Gupta, who works as a Machine Learning Researcher at Cytel in Canada. The interview gives you a deep dive into black-box models and transparent machine learning, and how the latter is becoming more important in clinical research today.

On March 21, Cytel conducted a webinar with Alind on, “Transparent Machine Learning in Oncology”. Alind presented our continuing work in immuno-oncology using Bayesian network models for predicting safety and survival outcomes, extrapolating from limited follow-up data and validating with external real-world data for key subgroups. Continue reading for key highlights from the webinar.

Register now to get free access to webinar slides and recording.

Cytel is hosting a webinar on Transparent Machine Learning in Oncology, on April 21, 2020. Our speaker, Alind Gupta, Machine Learning specialist, will provide insights on a particular transparent ML method called Bayesian networks, and how we have been using it for HEOR and other real world applications in oncology trials. As the adoption of machine learning is on the rise, we speak to Alind about the differences between black-box models and transparent machine learning, and how the latter is becoming more important in clinical research today. Alind also speaks about the application of ML on real-world data and how it is going to evolve in the coming years.

Machine learning (ML) aims to discover patterns from data that can be used for prediction, but the use of “black-box” ML models in healthcare research and decision-making has been limited, due to clinical liability and lack of trust from stakeholders. FDA guidelines for ML-based devices mandate transparency to assure continual safety and efficiency as notable recent failures have prompted increasing ML research into bias, fairness and causality. This has ramifications for all therapeutic areas but particularly within oncology.

In association with Statisticians in the Pharmaceutical Industry (PSI) , UCB and Cytel hosted a symposium on September 11, 2019 at UCB’s offices in Slough, Berkshire. The primary agenda was to educate the audience on Artificial Intelligence (AI) approaches and their impact on clinical development.

With recent advances in AI, it is important for quantitative scientists to keep up to date with the most recent methods and be involved in guiding their application to the most pressing analytical challenges. This one-day event covered cutting edge examples of how data science and statistical sciences are intersecting, and its relevance to our attendees.

“Artificial Intelligence and associated methodology is becoming increasingly important to the Pharma Industry and its technical foundation in statistical theory means that PSI is naturally keen to promote good practice through its membership and established Industry links. PSI is proud to have set up a Special Interest Group in this field and is keen to broaden its links and membership.”

- PSI Data Science special interest group

In this blog, we share some of the key takeaways from the symposium. If you are interested in attending similar sessions, you can check Cytel’s list of upcoming events here.

 
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 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.

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 explosion in healthcare information and “big data “has been one of the most written about topics in the last few years. These big data in the form of electronic health records, diagnostic tests, genomics, proteomics, not to mention data from wearable devices and apps have the potential to transform healthcare.  That potential can only be realized though through the application of advanced analytics to recognize patterns from the vast information available. As such, disciplines such as pattern recognition play a pivotal role in the future of healthcare.