Cytel Blog

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.