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.
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Staying abreast of the rapid pace of clinical development means adopting innovative or computationally intensive designs like Bayesian methods. These methods allow for the incorporation of prior knowledge, in terms of either expert opinion from clinicians or historical data, in statistical inference. Thus, they have the additional advantage of being able to work with real-world data (generally, real-world data has a lot of missing data) without the need to impute missing values. These kinds of models are also flexible enough to work with temporal data. This helps ease the reliance on large sample approximations that are often required for frequentist methods and generally results in greater efficiency in study design.
In this edition of The Informative Bayesian by Pantelis Vlachos, we learn about information borrowing to form a prior distribution. In a Bayesian framework, borrowing from historical data is equivalent to considering informative priors. These priors can be derived as meta-analytic predictive (MAP) priors or using patient-level data.
Keeping up with the rapid pace of clinical development means that we need to adopt the innovative or computationally intensive designs like Bayesian methods. Yet, cutting edge technology can sometimes be difficult to assess or can introduce risk. Cytel’s new web-native extension of East, East AlloyTM, makes it practical and sustainable to adopt innovative and computationally intensive designs. Continue reading this blog to learn more.
Pantelis Vlachos, Principal, Strategic Consultant at Cytel, conducted a webinar to introduce the capabilities of East AlloyTM. East Alloy is a new East environment that enables rapid access to innovation with the trust and support you have come to expect from Cytel. The cloud-native software makes it practical to apply computationally intensive Bayesian methods. Download the brochure to learn more.
This blog is a part of the new blog series on technology and Bayesian decision-making by Pantelis. Continue reading to learn about the methods and capabilities, such as, Bayesian meta-analytic priors, Bayesian MAMS, adaptive dose-finding and others, available to all East Alloy users.
Cytel brings to you a new blog series on technology and Bayesian decision-making by Pantelis Vlachos, Principal/Strategic Consultant for Cytel. In his inaugural post Pantelis walks us through the features and benefits of our new offering, East Alloy™. East Alloy™ is a web-based extension of East for clinical trial design that blends the pace of SaaS delivery, the ease of use and robustness of Cytel software, and the velocity of cloud-based computing. Gain some behind-the-scenes insights into the development of this new module and understand how your company can leverage East Alloy to conduct computationally intensive designs with ease, confidence, and speed.
Cytel is conducting a webinar series that focuses on target trial emulation and causal inference approaches using real world data. In collaboration with Dr. Miguel Hernán, Professor at Harvard University, Cytel is pioneering two “Head-to-Head Comparisons using Real World Data” studies, one in oncology one and in cardiovascular disease. These projects will occur in real time across this webinar series. Our presenters for the first webinar in this series were Dr. Miguel Hernán and Devon Boyne, Director of Epidemiology at Cytel. Continue reading this blog for a summary of the webinar, “Head to Head Comparisons Using Real World Data - The Time for Causal Inference is Now” conducted on July 7, 2020. Click on the button to access the webinar replay.
We also had the opportunity to interview Dr. Hernan on head-to-head comparisons. Read the interview here.
Supposing two treatments, A and B, need to be compared that have not been compared through a clinical trial. In the absence of such information, those treatments have been compared with each other via a third treatment, C (i.e., A to C and B to C) using indirect treatment comparison approaches. Recent developments are challenging this status quo. The increased availability of regulatory-grade RWD helps. We can also now avoid some of the biases that used to plague the use of observational data.
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.
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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.