A number of trials recently disrupted by the COVID-19 pandemic are now in the process of re-assessing recruitment timelines, and establishing new recruitment targets. Recruitment projections, though, are only as good as the starting assumptions with which a forecast is made and the models subsequently used to calculate timelines.
Overly simplistic assumptions, even familiar ones like, “All patients will arrive and enroll in a strictly linear fashion,” have strong drawbacks. In order to maximize the potential to achieve recruitment milestones and to avoid misleading trial projections, planning must take careful precautions even when conditions are not this uncertain.
Along with starting assumptions, the mathematical models that underpin forecasts and simulations play an important role in ensuring your team is oriented towards the correct recruitment targets. Monte Carlo methods, for example, are widely used in industries like finance to make projections, and can be easily appropriated by pharmaceutical development for accurate predictions in the pandemic era.
The Cytel Whitepaper on Simulations for Patient Recruitment provides a high-level account of how to ensure recruitment targets are met with sound starting assumptions and key modelling techniques.