Modeling Clinical Outcomes in Prostate Cancer: Application and Validation of the Discrete Event Simulation Approach

Abstract

Objectives: Treatment landscape in prostate cancer has changed dramatically with the emergence of new medicines in the past few years. The traditional survival partition model (SPM) cannot accurately predict long-term clinical outcomes because it is limited by its ability to capture the key consequences associated with this changing treatment paradigm. The objective of this study was to introduce and validate a discrete-event simulation (DES) model for prostate cancer.

Methods: A DES model was developed to simulate overall survival (OS) and other clinical outcomes based on patient characteristics, treatment received, and disease progression history. We tested and validated this model with clinical trial data from the abiraterone acetate phase III trial (COU-AA-302). The model was constructed with interim data (55% death) and validated with the final data (96% death). Predicted OS values were also compared with those from the SPM.

Results: The DES model's predicted time to chemotherapy and OS are highly consistent with the final observed data. The model accurately predicts the OS hazard ratio from the final data cut (predicted: 0.74; 95% confidence interval [CI] 0.64-0.85 and final actual: 0.74; 95% CI 0.6-0.88). The log-rank test to compare the observed and predicted OS curves indicated no statistically significant difference between observed and predicted curves. However, the predictions from the SPM based on interim data deviated significantly from the final data.

Conclusions: Our study showed that a DES model with properly developed risk equations presents considerable improvements to the more traditional SPM in flexibility and predictive accuracy of long-term outcomes.

Keywords: discrete event simulation; modeling; prostate cancer; treatment sequence.