Peter Gibbs1,2, Maarten IJzerman3,4,5, Koen Degeling6,7, Hui-Li Wong1,5, Hendrik Koffijberg3, Azim Jalali1, Jeremy Shapiro8, Suzanne Kosmider2, Rachel Wong1,9,10, Belinda Lee1,5,11, Matthew Burge12, Jeanne Tie1,5,2, Desmond Yip13, Louise Nott14, Adnan Khattak15, Stephanie Lim16, Susan Caird17. 1. Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia. 2. Department of Medical Oncology, Western Health, Melbourne, VIC, Australia. 3. Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands. 4. Cancer Health Services Research, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia. 5. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. 6. Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands. koen.degeling@unimelb.edu.au. 7. Cancer Health Services Research, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia. koen.degeling@unimelb.edu.au. 8. Department of Medical Oncology, Cabrini Health, Melbourne, VIC, Australia. 9. Department of Medical Oncology, Eastern Health, Melbourne, VIC, Australia. 10. Eastern Health Clinical School, Monash University, Box Hill, VIC, Australia. 11. Department of Medical Oncology, Northern Health, Melbourne, VIC, Australia. 12. Department of Medical Oncology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia. 13. Department of Medical Oncology, The Canberra Hospital, Canberra, ACT, Australia. 14. Department of Medical Oncology, Royal Hobart Hospital, Hobart, TAS, Australia. 15. Department of Medical Oncology, Fiona Stanley Hospital, Perth, WA, Australia. 16. Department of Medical Oncology, Campbelltown Hospital, Campbelltown, NSW, Australia. 17. Department of Medical Oncology, Gold Coast University Hospital, Gold Coast, QLD, Australia.
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.
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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