Clinical trial enrollment is the period in drug development that is often cited as the most variable and unpredictable.
In today’s clinical world, we are seeing trials become larger and more complex making the task of accurately forecasting patient recruitment and site initiation increasingly more difficult.
One of the obstacles making this exercise difficult is the many factors that go into clinical trial enrollment.
Study Design Elements (Known)
Global Region, Phase and Therapeutic Area can have a significant effect on site availability and patient population. A protocol’s list of inclusion/exclusion criteria will also alter enrollment. Some other key factors include:
- Size of the study
- Procedure frequency
- Visit schedule
Study Design Elements (Unknown)
The above factors are common known elements during the design period of any clinical trial, but where things get more interesting is when trying to forecast unknown factors such as patient/site enrollment rates.
Traditional approaches to forecasting these elements are based on intuition and prior experience and while this is sometimes valid, it may be inconsistent and inefficient.
No two trials are ever the same and with so many variable factors being thrown into this problem, a more analytical approach may be the solution.
Accurately predicting study enrollment period, site count, patient recruitment rate, screen failures, drop out rates and completion rates are invaluable metrics during the design period of a study and can save a study manager a significant amount of time and money.
Using advanced statistical approaches, we can take the most common elements of study design and predict, at a high level of accuracy, these unknown elements making the entire clinical trial enrollment period more efficient and effective.
Multivariate Regression Analysis, Neural Networks and Time Series Trending are some techniques used that enable us to build statistical models to identify the clinical variables most suited to predict useful outcomes.
For example, did you know that your targeted number of enrolled patients along with the planned duration of your clinical trial are two of the best predictors for site count? Qualitatively, this may seem quite intuitive, but statistical modeling allow you to put an actual, quantitative value on these factors to produce a tangible site count used for planning purposes.
Wait, so you can tell me how many sites I should target during my design period? YES. OK, but this doesn’t tell me which sites are high performing and most effective….
Using an extensive collection of global site performance data, you can pinpoint which sites would be most effective given your specific needs and budgeting factors.
Site recruitment and performance KPIs such as site experience, initiation rate, available patient population, data query rates and cost per patient are vital to choosing the site most fitted for your trial.
Through the development of a site scoring algorithm along with the site factors that matter most to your specific trial and budget, you can narrow down the overwhelming amount of institutions and principal investigators to a select few that will streamline your site selection process.
Is keeping cost down a primary goal or is high data quality more important? Does global region matter or is fast enrollment something you want to optimize?
By adjusting the sensitivity of these driving factors in your algorithm, you will be able to gain access to a list of the best candidates.
In this new era of big data and advanced analytics, the age of the guessing game is over. The tools and technologies that are now available afford their users greater predictability, reduced cycle times and improved efficiencies.
Taking the guess work out of this aspect of trial design allows study managers to focus their efforts and resources on other important trial elements. So, with all this knowledge around trial enrollment, will you stop playing the guessing game? My guess is you will.