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

Koen Degeling (), Hui-Li Wong, Hendrik Koffijberg, Azim Jalali, Jeremy Shapiro, Suzanne Kosmider, Rachel Wong, Belinda Lee, Matthew Burge, Jeanne Tie, Desmond Yip, Louise Nott, Adnan Khattak, Stephanie Lim, Susan Caird, Peter Gibbs and Maarten IJzerman
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Koen Degeling: University of Twente
Hui-Li Wong: Walter and Eliza Hall Institute of Medical Research
Hendrik Koffijberg: University of Twente
Azim Jalali: Walter and Eliza Hall Institute of Medical Research
Jeremy Shapiro: Cabrini Health
Suzanne Kosmider: Western Health
Rachel Wong: Walter and Eliza Hall Institute of Medical Research
Belinda Lee: Walter and Eliza Hall Institute of Medical Research
Matthew Burge: Royal Brisbane and Women’s Hospital
Jeanne Tie: Walter and Eliza Hall Institute of Medical Research
Desmond Yip: The Canberra Hospital
Louise Nott: Royal Hobart Hospital
Adnan Khattak: Fiona Stanley Hospital
Stephanie Lim: Campbelltown Hospital
Susan Caird: Gold Coast University Hospital
Peter Gibbs: Walter and Eliza Hall Institute of Medical Research
Maarten IJzerman: University of Twente

PharmacoEconomics, 2020, vol. 38, issue 11, No 9, 1263-1275

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1007/s40273-020-00951-1

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