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Interview with Yannis Jemiai: Advanced Design Framework

The widespread use of cloud-computing has altered the clinical trial design process. Whereas three or four years ago, it would take a statistician perhaps two or three days to design five clinical trial designs, a well-resourced statistician can now simulate and model well over 100,000 designs in less than 30 minutes. How does this affect the process of designing clinical trials

According to Yannis Jemiai, Chief Scientific Officer at Cytel, a combination of technology and process changes can establish the foundation for significant increases in productivity. Yannis argues that uncertainty should not be viewed as a challenge but an opportunity. Using statisticians strategically as well as tactically throughout the design process can help R&D teams drive commercial value for greater speed, savings and success.

Yannis discusses: how to explore an expansive design space thoroughly without settling for suboptimal clinical trial designs; how to decide together on the benefits of these hundreds of thousands of designs using a quantitative evaluation framework; and how the complex tradeoffs across operational parameters can be easily communicated using visual tools and a collaborative decision platform.

Listen to the podcast by clicking below.

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Excerpts from the Podcast:

1: On Advanced Innovative Designs
“These designs were represented as being able to deliver higher levels of statistical rigour with fewer resources, accelerated timelines, higher productivity and so forth. For almost a decade we have been hearing about these fantastic capabilities, and there’s no doubt that mathematically we should be seeing them. So why haven’t we?”

2: On Making Strategic Use of Statisticians
“You need to see statisticians as strategic collaborators. After all, half the challenge is to take advantage of uncertainties, and make sure that risks go in your favor whenever possible. How can you do that without a statistician?”


"Traditionally, you would design every trial separately, then do pairwise comparisons of each with really rigorous math. This made it difficult to say, “Well what if we push back the interim look three weeks? Will this affect probability of success to the point that we would choose this design over another?”

We need to be able to visualize all the trials at once, and communicate tradeoffs quickly…

When you have such a high-stake trial, people naturally favor the designs with which they have the most familiarity. We need to move beyond this, which is the aim of the Advanced Design Framework.

To learn more about the Advanced Design Framework click here.


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