Simulating Progression-Free and Overall Survival for First-Line Doublet Chemotherapy With or Without Bevacizumab in Metastatic Colorectal Cancer Patients Based on Real-World Registry Data
AuthorDegeling, K; Wong, H-L; Koffijberg, H; Jalali, A; Shapiro, J; Kosmider, S; Wong, R; Lee, B; Burge, M; Tie, J; ...
PublisherADIS INT LTD
University of Melbourne Author/sGibbs, Peter; Wong, Rachel; IJzerman, Maarten; Degeling, Koen; Tie, Jeanne; Lee, Belinda; Wong, Hui-Li
Medical Biology (W.E.H.I.)
Melbourne School of Population and Global Health
Document TypeJournal Article
CitationsDegeling, K., Wong, H. -L., Koffijberg, H., Jalali, A., Shapiro, J., Kosmider, S., Wong, R., Lee, B., Burge, M., Tie, J., Yip, D., Nott, L., Khattak, A., Lim, S., Caird, S., Gibbs, P. & IJzerman, M. (2020). Simulating Progression-Free and Overall Survival for First-Line Doublet Chemotherapy With or Without Bevacizumab in Metastatic Colorectal Cancer Patients Based on Real-World Registry Data. PHARMACOECONOMICS, 38 (11), pp.1263-1275. https://doi.org/10.1007/s40273-020-00951-1.
Access StatusAccess this item via the Open Access location
Open Access URLPublished version
BACKGROUND: Simulation models utilizing real-world data have potential to optimize treatment sequencing strategies for specific 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 first-line doublet chemotherapy with or without bevacizumab for specific 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 reflect parameter uncertainty. RESULTS: The survival models showed good calibration based on the regression slopes and modified Hosmer-Lemeshow statistics at 1 and 2 years, but not for short-term predictions at 0.5 years. Modified C-statistics indicated acceptable discrimination. The simulation estimated that median first-line PFS (95% confidence 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 specific subpopulations, with outcomes suggesting treatment could be optimized. Although results based on real-world data are informative, they cannot replace randomized trials.
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