Simulating progression-free and overall survival for first-line doublet chemotherapy with or without bevacizumab in metastic colorectal cancer patients based on real-world registry data

Background: Simulation models utilizing real-world data have potential to optimize treatment sequencing strategies for specifc patient subpopulations, including when conducting clinical trials is not feasible. We aimed to develop a simulation model to estimate progression-free survival (PFS) and overall survival for frst-line doublet chemotherapy with or without bevacizumab for specifc subgroups of metastatic colorectal cancer (mCRC) patients based on registry data. Methods: Data from 867 patients were used to develop two survival models and one logistic regression model that populated a discrete event simulation (DES). Discrimination and calibration were used for internal validation of these models separately and predicted and observed medians and Kaplan-Meier plots were compared for the integrated DES. Bootstrapping was performed to correct for optimism in the internal validation and to generate correlated sets of model parameters for use in a probabilistic analysis to refect parameter uncertainty. Results: The survival models showed good calibration based on the regression slopes and modifed Hosmer-Lemeshow statistics at 1 and 2 years, but not for short-term predictions at 0.5 years. Modifed C-statistics indicated acceptable discrimination. The simulation estimated that median frst-line PFS (95% confdence interval) of 219 (25%) patients could be improved from 175 days (156-199) to 269 days (246-294) if treatment would be targeted based on the highest expected PFS. Conclusions: Extensive internal validation showed that DES accurately estimated the outcomes of treatment combination strategies for specifc subpopulations, with outcomes suggesting treatment could be optimized. Although results based on real-world data are informative, they cannot replace randomized trials.