Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data

Area of Science:

• Clinical trial management
• Statistical modeling in healthcare
• Pharmacoeconomics

Background:

• Accurate clinical trial enrollment timeline forecasting is critical for strategic decision-making and operational efficiency.
• Traditional methods like naive averaging or simple Poisson-Gamma models fail to incorporate crucial time- and location-dependent factors.
• Existing approaches lack the sophistication to handle the complexities of modern clinical trial demands.

Purpose of the Study:

• To introduce a novel, accurate, and efficient two-segment statistical approach for clinical trial enrollment timeline forecasting.
• To develop a predictive model that integrates study-level and historical organizational data for prospective enrollment predictions.
• To validate the proposed model's performance against established frameworks using real-world clinical trial data.

Main Methods:

• A two-segment statistical approach utilizing Quasi-Poisson regression for subject accrual rates.
• A Poisson process model for simulating subject enrollment and site activation dynamics.
• Integration of publicly accessible study data with internal historical data for enhanced predictive power.

Main Results:

• The proposed model demonstrates superior accuracy and efficiency compared to naive and traditional statistical methods.
• Validation on seven curated studies confirms the robustness and reliability of the new enrollment forecasting framework.
• The model effectively incorporates time- and location-specific variables, leading to more precise timeline predictions.

Conclusions:

• The novel two-segment statistical approach offers a significant advancement in clinical trial enrollment forecasting.
• This framework provides a more accurate and reliable tool for strategic planning and execution excellence in clinical trials.
• The model's ability to integrate diverse data sources enhances its practical applicability for pharmaceutical organizations.