Accurate enrollment forecasting is a cornerstone of successful clinical trials — yet it remains one of the most difficult elements to control. Inaccurate enrollment forecasts can lead to missed timelines, costly amendments, and even trial failure.

One approach to control uncertainties in enrollment is to design studies that perform sufficiently well against a variety of potential enrollment scenarios. This topic was at the heart of a recent project completed by Edoardo Lisi, Statistics Leader, Carly Barnett, Associate Director of Biostatistics, and Paula Curtis, Principal Statistician, at GSK. They leveraged Explore, a simulation-guided trial design tool within the East Horizon™ platform, and its new functionality for integrating custom R code — a widely used and popular programming language — into native software workflows.

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