W-33: Promoting a Data-Driven Simulation Model to Enhance Quality in Rare Disease Clinical Trials

Poster Presenter

Benjamin Moody

Clinical Analytics and Simulations Lead
QuintilesIMS
United States

Objectives

Developing a quantitative strategy to accurately model the design and operationalization of a rare disease clinical trial to enhance trial execution timelines and quality.

Method

Application of a probability analysis simulation model, using data-driven metrics, to ensure targeted projections, to recruit rare patients at highly specialized centers with variable experience in clinical trials execution.

Results

The various clinical trial scenarios reported contribute to an operationally focused approach to rare disease clinical development based on quantitative scenario modeling. This approach assisted with reducing attrition, increasing patient recruitment efficiency, predicted trial duration and overall quality. These data enabled forecasting with flexible and adjusted enriched scenario planning, while considering the influence of operational and disease-specific variables in a drug development area challenged with limited retrospective and epidemiological data.

Conclusion

Drug development timeframes are long and complex. Integrating a multidimensional, quantitatively-based approach to rare disease clinical trial optimization informs go/no-go decisions based on a priori specifications. This methodology serves as an analytical tool to allow for incorporation of such design approaches which enable recruitment of the right patient at the right clinical trial site at the right time. Novel strategic data-driven approaches are becoming the new norm for demonstrating successful and qualitative delivery of elaborate and complex clinical development programs.