The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.

Podcast: Enhancing Patient Enrollment Forecasting with EnForeSys 2.0

September 5, 2018

 

EnForeSys is Cytel’s tool for patient recruitment planning. We have discussed on the blog recently with Tufts University's Center for the Study of Drug Development, Ken Getz, the problem the industry continues to face with patient recruitment, and the fact that most trials significantly exceed their original planned duration. In the face of this problem there's a pressing need to create more realistic plans and scenarios. To achieve this, EnForeSys models the enrollment process and then assigns probabilities for various scenarios.

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Interview with Ken Getz: Exploring Challenges of Clinical Trial Operations Part 2

April 13, 2018

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. 

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Interview with Ken Getz: Exploring Challenges of Clinical Trial Operations Part 1

April 5, 2018

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. 

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When Biostatisticians Disagree About Ethics

July 6, 2017

 By Esha Senchaudhuri

The ethical benefits of adaptive clinical trials have been widely acclaimed: higher prospects for patients to be enrolled into the correct trial arm [1]; shorter trials for the most effective new therapies (see the early stopping outcome of the MUSEC trial) [2]; and enrollments commensurate with the needs of research, i.e. the last patient enrolled is not superfluous to a trial’s outcomes (e.g. according to one clinical biostatistician, “trial designs that learn more and treat better with less burden and sacrificing of patients”) [3].

However, the acknowledgement that ethical benefits exist is a separate question from the degree to which they exist when compared to a more traditional design.

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Innovative Phase 3 Adaptive Enrichment Design in Oncology

December 2, 2016

At a recent Pfizer/ Cytel seminar on rare disease and oncology development, Cytel’s Lingyun Liu presented innovative work on a patient enrichment design. In this blog, we share some design and operational considerations. This approach can help mitigate against underpowering of a clinical trial where there is uncertainty and heterogeneity of treatment effect among subpopulations.

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Overcoming challenges of 'Less Well Understood' Adaptive Designs

September 7, 2016

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. 

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EnForeSys® enrollment forecasting showcases at SCOPE

February 17, 2016

Charles Liu, Statistician and Product Manager at Cytel will be part of the expert speaker panel at the 7th Annual SCOPE Summit on 23-26th February. This year’s meeting is taking place in Miami, and offers a packed program with tracks covering such varied topics as risk based monitoring, clinical data technology and integration, and managing outsourced clinical trials. SCOPE has become one of the leading events on the conference calendar for clinical operations executives, with 1100 delegates from over 300 companies expected to attend this February. 

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3 Questions to Ask About Your Trial's Enrollment

July 2, 2015

 

Clyde Haberman, a columnist for the New York Times, once commented on the remarkable consistency of train arrival times on the Tokyo subway: "Every station lists the scheduled arrival times: 9:01, 9:04, 9:08 and so on. I lived in that city for five years...I never saw a train arrive so much as a minute late, not once. A posting of 9:01 meant 9:01." [1]. Such predictability is rarely observed in the messy world of clinical operations, yet many study plans are formulated like a Tokyo subway timetable. In a previous blog entry [2], we cited an example trial that targeted 1,800 patients across 50 sites over a 10-month period. Let us examine three underlying assumptions in this plan, with the help of a modeling and simulation tool.

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Patient Recruitment Feasibility: Would you bet $12 million dollars on it?

June 4, 2015

Two insightful papers from Applied Clinical Trials should be of interest to many clinical trial planners. The first by Kenneth Getz describes the problem of enrollment performance, while the second by Matthew Kibby proposes a potential solution.

The Facts

Getz reports a study providing recent estimates of industry-wide rates of enrollment delays [1]. In 2012, the Tufts Center for the Study of Drug Development (CSDD) requested data from 10 pharmaceutical companies and two CROs. The combined database covered nearly 16,000 investigative sites involved in 151 clinical trials from years 2008-2010. A few significant findings are worth highlighting:

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