Slow subject recruitment and poor retention are perpetual problems for the pharmaceutical industry. According to industry
estimates, delays caused by recruitment affect most studies and end up costing hundreds of thousands to millions of dollars.
Biopharmaceutical companies concerned with achieving their Last Patient In (LPI) targets should focus on making their subject
recruitment and retention strategies, planning, and execution far more effective through a combination of technology, data
assets, and process changes. Through appropriate use of technology platforms to bring together disparate information from
across the organization, biopharmaceutical companies can better predict and manage recruitment and retention through data-driven
frameworks.
Access to the right data allows trial sponsors to more accurately address three vital questions about subject recruitment:
- What are the likely enrollment rates for a particular clinical trial?
- How will known development issues, such as staggered study startup and holiday periods, impact my LPI milestones?
- Who are the best investigators to achieve the required recruiting objectives?
Enrollment rates
Information concerning the feasibility or difficulty of subject recruitment for specific types of subjects, indications, and
sites directly impacts the accuracy of projected enrollment rates. By identifying appropriate data assets to support feasibility
studies and site selection, sponsors can make the right decisions about trial staffing, budget, and timelines and positively
impact subject recruitment. Feasibility should be driven by a triangulation process involving three major elements:
- previous recruitment results from similar trials (internally generated or via literature reviews)
- internal and external experts
- investigator surveys.
A database of previous experiences that includes key information such as eligibility criteria, study synopses, and enrollment
rates by country are critical to determining the relevance of a previous trial's results on the current trial. An analysis
of the similarities between eligibility criteria, the drug, procedures, and standard of care can be used to modify the enrollment
rate of the previous trial to approximate what can be expected in the new study.
This internal data can be augmented with published literature, particularly in areas where there is little relevant experience
within the company and/or in indications where a substantial number of similar trials have been published. Most published
studies provide enough data to allow for an approximate enrollment rate to be developed, but they tend to play a secondary
role due to the lack of detailed information around enrollment time frames at the site level or specific eligibility criteria
that is needed to develop a more refined estimate.
Data from previous trials can be used as part of a formal analysis process, conducted by internal and external experts. These
experts should have a firm grasp of the standard of care in each relevant country (past, current, and medium-term future)
and the clinical trials environment, and they should also have access to epidemiological data. Through the combination of
these elements and the historical trial data, a reasonably tight range of most likely enrollment rates can be developed.
Finally, this process should be compared to the results of a survey of potential investigators in each relevant country. This
process is typically blinded and asks relevant questions about such topics as likely enrollment rates, IRB/EC issues, and
other factors that may influence the conduct of the trial. This process plays a critical role in refining the analysis described
earlier and identifying potential high enrollers. A reduction factor, however, must be applied to the enrollment results from
the sites to account for the lack of complete data about the trial, lack of time for site staff to carefully think through
the survey, and the level of irrational exuberance that's typically contained in the responses—a 50% reduction of the enrollment
estimates is often appropriate.
This entire process should be coordinated by a central group that is separate from the trial team. This allows for both process
flow management efficiency and a level of dispassionate estimation that is critical to accurate planning.
