Enrollment Planning for Critical Path Studies


Applied Clinical Trials

Stopping the madness behind the current methods in enrollment planning.

There is a humorous family photo I keep at my desk. It was my fifth birthday and I was gifted a new swing set intended for use by my friends and me at my birthday party. Now, rather than have it delivered and assembled, my father decided that he would gather his friends and they would assemble it at the start of my party. Three hours later, a photo was taken. This photo was of several uber intelligent people; lawyers, judges, engineers…all standing around looking perplexed, scratching their heads, confused by the “extra parts,” while frantically looking for missing pieces. The swing set never made its debut at my party, however, the photo has served me quite a few laughs over the years.

Fast forward 20 years and I am in my first R&D planning meeting. A critical path study is the topic of conversation…a blockbuster in the making. On the speakerphone is the therapeutic area head, questioning the project lead on the rationale and methods behind the forecasted enrollment milestones and projections. Once again I am taken aback as I observe the silence, and unlike the humor of the scenario in my photo, I am shocked as I watch the management team around the table stare at one another with bewildered looks. Their answer? Quite simply, enrollment forecasts were based on enrollment estimates provided by the sites…cut in half. And even more concerning, this answer was acceptable!

That meeting was almost 15 years ago and I am embarrassed to say that, for most of the industry, very little progress has been made in clinical trial enrollment planning and forecasting since. Just a few months back, a client from a top 5 pharma company approached me with the same “methodology” for how they developed their enrollment projections—site estimates, cut in half. We all have seen the ramifications of this make-shift approach to planning.

The implications are devastating not only to those of us inside R&D, but to those who serve a supporting role, such as the research physician struggling to justify the financial loss to his practice against the benefit of delivering novel therapies to his patients. Industry now suffers over 80% of trials delayed, with the major contributor to delays being failure to reach enrollment on time. As R&D struggles to maintain both pipeline and bottom line viability, we are forced to sit on the sidelines and watch the mega R&D mergers toss our colleagues into an over flowing pool of industry created mass reductions. It’s time to stop the madness.

So where do we go from here? Over the last decade we have made significant investments in technology, with the implementation of CTMS and EDC successfully reducing the chaos of our paper based world by offering real-time access to study level and patient level data. Our ability to now capture and share data in real time has improved patient safety, ensured uniformed data capture and provided an opportunity to carry out multicountry studies in a dependable and transparent fashion.

However there are limitations to these systems. While we have addressed the time and cost issues associated with prior paper-based inefficiencies, the current offering of a real time, yet stagnant snapshot does little to assist the average operations manager in pressure testing preliminary planning assumptions or in the forecasting of accurate study milestone completion. With increased globalization of clinical trials and the growing complexities of trial designs, it’s time we optimize our processes with a solution that we can firmly hang our hat on.

Predictive analytics offer an opportunity to empower our clinical teams with a strategic and simplified way to leverage our real-time data into an optimized enrollment plan. By creating a solution that provides more predictable clinical enrollment we can:

• Overcome the overwhelming amount of meaningless raw data and reduce the amount of subjective planning by offering a standardized method to planning and forecasting
• Utilize today’s data in a practical and tactical method to predict tomorrow’s performance
• Remove the current silo structured communication environment and improve global team relations by allowing clinical teams the flexibility and transparency of a web-based, centrally located system that incorporates all data into a clean, simplified, easy to understand, planning and analysis application
• Enforce the application of data driven decision making without the burden of time consuming manual analysis
• Offer an enhanced view of enrollment data and projections with standardized methods for reporting to upper management
• Implement a consistent methodology across the organization that translates easily between middle and upper management
• View real time and ongoing impact of actual performance to date on key milestones
• Model multiple scenarios, before and during enrollment, for impact against timelines prior to recommending and implementing a solution
• Reduce cycle time variance (complete on or ahead of plan), decrease costs (drive additional efficiencies in study management), increase revenue (increased capacity allows for greater throughput in the pipeline), and offer life saving critical path therapies to market faster

With over 80% of trials delayed [1], and a potential blockbuster loss of $32M per day in worldwide sales (based on worldwide daily sales of Lipitor, 2004), a one-month delay is equivalent to the annual salaries of about 6400 R&D operations managers. We have a choice, either we can insist our organizations invest in more advanced analytics and tools to aid in our ability to successfully manage enrollment, or we can anticipate joining our 23,300 colleagues reduced from our workforce last year. Let’s hope if it’s the latter, our ability to forecast our own life plans far exceeds that of R&D.


1. Centerwatch

April Lewisis the director of enrollment services at DecisionView, Inc., 330 Townsend St., Suite 234, San Francisco, CA 94107.

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