Being Transparent About Brilliant Failures: An Attempt to Use Real-World Data in a Disease Model for Patients with...

Area of Science:

• Oncology
• Health Economics
• Data Science

Background:

• Real-world disease models can inform the cost-effectiveness of treatment sequences in clinical practice.
• Evaluating treatment sequences requires accurate models of disease progression and outcomes.

Purpose of the Study:

• To assess if a disease model built solely on real-world data (RWD) can estimate treatment effectiveness for castration-resistant prostate cancer (CRPC).
• To determine the suitability of such a model for cost-effectiveness analysis in CRPC.

Main Methods:

• A patient-level simulation model was developed using data from the Dutch CAPRI registry.
• Time-to-event and overall survival were estimated using multivariate regression.
• Model validity was tested by comparing simulated outcomes with observed registry data.

Main Results:

• Simulated patient characteristics and overall survival were comparable to observed data (median OS 20.6 vs. 19.8 months).
• The model showed less accuracy in estimating survival differences between specific treatments (e.g., abiraterone acetate plus prednisone, enzalutamide).

Conclusions:

• The disease model accurately reflected overall CRPC population data but could not predict treatment-specific survival differences.
• The model's inability to differentiate treatment outcomes makes it unsuitable for CRPC cost-effectiveness analysis.
• Combining RWD with randomized controlled trial data may enhance model accuracy for treatment effectiveness estimation.