Forecasting clinical trial success using anonymized external expert panels.

Area of Science:

• Drug development and clinical trial management
• Biostatistics and predictive modeling
• Pharmaceutical investment and risk assessment

Background:

• Accurate prediction of clinical trial probability of success (POS) is vital for drug developers and financial investors.
• Existing POS forecasting methods used by pharmaceutical companies often struggle with accuracy, bias, and scalability.
• There is a need for improved, reliable methods to forecast clinical trial outcomes.

Purpose of the Study:

• To introduce and evaluate a novel approach for forecasting the probability of success (POS) in clinical trials.
• To address the limitations of current POS prediction methods in terms of accuracy, bias, and scalability.
• To present a broadly applicable solution for trial-specific POS forecasting.

Main Methods:

• Development and implementation of a framework utilizing external expert panels for POS forecasting.
• Systematic evaluation of the expert panel approach against established criteria for accuracy, bias, and scalability.
• Application of the method across diverse clinical trial scenarios and therapeutic areas.

Main Results:

• The external expert panel approach demonstrated significant promise in improving POS forecast accuracy.
• The proposed method showed enhanced freedom from bias compared to traditional techniques.
• The approach proved to be scalable, adaptable to various stakeholders and trial types.

Conclusions:

• External expert panels offer a viable and effective alternative for predicting clinical trial success.
• This method addresses key challenges in current POS forecasting, enhancing reliability for decision-making.
• The approach has broad applicability for stakeholders involved in clinical trial development and investment.