The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Interview with Ken Getz: Exploring Challenges of Clinical Trial Operations Part 2
We return to our discussion with Ken Getz of the Tufts CSDD for part 2 of our blog post on key challenges in clinical trial operations. You can find Part 1 of the interview here, or read on to gain his insights on the fundamental problem at the heart of clinical trial operations challenges, and his views on the initiatives and programs that he believes show the most promise for the future.
Interview with Ken Getz: Exploring Challenges of Clinical Trial Operations Part 1
Photo by J. Kelly Brito on Unsplash
Research on clinical trial enrollment makes for sobering reading, characterized by the oft-cited statistic that 11% of active sites fail to enroll a single patient. In this first part of a two part interview, we sit down for a discussion with Ken Getz of the Tufts CSDD. Here, Mr. Getz expands on some of the Center’s more recent research on challenges in clinical trial operations. In the second part, to be published next week, Mr. Getz will reveal his insights on the key opportunities for the future.
5 Scenarios When ‘Keep it Simple’ May Be Bad Advice for Clinical Trial Designers
When designing clinical trials, many trial designers are advised to keep the trial simple. Prima facie, the keep it simple principle seems like sound advice. There are various logistical uncertainties that arise when implementing a clinical trial, and the more simple a trial – so conventional wisdom says – the easier it is to respond to these uncertainties.
According to Zoran Antonijevic, a Senior Director at Cytel Consulting, there is reason to doubt such conventional wisdom. After all, flexibility is hardly a virtue of a traditional trial design. Simple designs may seem to make it easier to monitor data and report results. However, a flexible design can better address remaining uncertainties in product development. These uncertainties are related to treatment effect, dose selection, or a sub-population that would experience the best benefit/risk from the treatment.
Predicted Interval Plots: A General Overview
In anticipation of Cyrus Mehta’s webinar next week on new predictive analytics tools for trial forecasting, we thought we might give you a few introductory notes on the nature and purpose of Predicted Interval Plots (better known as PIPs).
What are PIPs?