Cytel Blog

Cytel data scientists apply advanced statistical techniques including predictive modeling of biological processes and drug interactions to unlock the potential of big data.

In this blog we talk to Munshi Imran, who is based in Pune, India to find out more about his career path, current role at Cytel and his interests outside of work.

In this blog we turn to some reading matter, and interview Gautier Paux and Alex Dmitrienko about the recent book 'Clinical Trial Optimization with R'.  The book explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. To learn more, read on for Paux and Dmitrienko's insights.

In the 2010 draft FDA ‘Guidance for Industry on Adaptive Design Clinical Trials for Drugs and Biologics',  the agency makes an important distinction between ‘well understood’ and ‘less well understood’ adaptive designs.

‘Well understood” adaptive designs may include such approaches as adaptation of eligibility criteria, adaptation for stopping early and adaptations to maintain study power based on blinded interim analyses of aggregate data. For these 'well-understood designs', there is little concern from the FDA about their potential to be implemented in adequate and well-controlled trials.  On the other hand, at the time of the drafting of the guidance at least, ‘ less well understood designs' (which include such approaches as adaptations for dose selection studies, adaptation of patient population based on treatment-effect estimates,  and adaptation for end-point selection based on interim estimates of treatment effect) gave greater concern. Interestingly, the FDA  Adaptive Designs for Medical Device Clinical Studies : Guidance for Industry and Food and Drug Administration Staff does not adopt this distinction. 

A recent article, Addressing Challenges and Opportunities of “Less Well-Understood” Adaptive Designs (He et al 2016) (1) takes a look at some of the perceived challenges of these designs and ways in which they may be overcome.  The publication is the result of work by a best practice sub-team formed by the DIA Adaptive Design Scientific Working group in January 2014. Cytel's Yannis Jemiai is a member of this group, and one of the co-authors of the article. 

In this blog, we take a look at a few of the challenges outlined and some of the suggested mitigations.  One aspect covered in the publication is seamless designs- and given the scope we'll devote a separate blog to this area. 

Last week, we were delighted to announce the release of East 6.4 bringing further cutting –edge approaches to the East user community. East is the industry standard platform for clinical trial design, simulation, and monitoring, improving scientific productivity during the critical planning stages of clinical development. In this blog we catch up with Yannis Jemiai, VP of Cytel  to gain some behind-the-scenes insights into the development and  new features of this important release.

Cytel has published a new whitepaper on Monte Carlo Simulations for Patient Recruitment, which illustrates how a technique already popular within industrial and business environments is now changing the game of data-driven patient enrollment forecasting. 

Recently, we published an interview with Chris Conklin, the Director of the Center for Feasibility Excellence at Pfizer. During the interview, Chris spoke about how his team navigates the complex terrain of trial planning and patient recruitment, and achieves those high flying enrollment milestones for each and every trial. His key message was to utilize modern methods in data-driven feasibility studies, augmenting historic and site-level data with new techniques in forecasting. 

Since our interview, Chris gave a talk at the annual SCOPE conference, in which he divulged a few more tips on obtaining consistent patient enrollment figures. An important feature was the use of Monte Carlo simulations, a popular tool from industrial and business operations, which is now gaining popularity amongst clinical operations specialists.

Monte Carlo simulations are easy to implement with the right tools, and yet can achieve target enrollment with 99% confidence. You can find below, a simple explanation of how this method works. Chris's slides (attached) contain a case study.    

Janus was the Roman God of transitions, a deity with two faces, one looking towards the past and the other the future. It was only a matter of time therefore, that clinical trial simulators, and other purveyors of predictive analytics would adopt him as an embodiment of their core initiatives. The aptly named Janus Initiative aims to model decision pathways in clinical development, using powerful simulations to help inform multiple stakeholders on adaptive licensing decisions. The objective is to give every stakeholder in the process a sense of what consequences his or her decision-making will have on other stakeholders involved.