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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

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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.

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Data and code availability statement

All R code, including intermediate and final outcomes, as well as a tool that allows exploration of dummy data similar to the TRACC data and simulations using the DES to be performed, are available online at https://personex.nl/research/mcrc-tracc/. Data from the TRACC registry is not publicly available.

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Author notes

  1. Peter Gibbs and Maarten IJzerman contributed equally.

Authors and Affiliations

  1. Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands

    Koen Degeling, Hendrik Koffijberg & Maarten IJzerman

  2. 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 & Maarten IJzerman

  3. Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia

    Hui-Li Wong, Azim Jalali, Rachel Wong, Belinda Lee, Jeanne Tie & Peter Gibbs

  4. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia

    Hui-Li Wong, Belinda Lee, Jeanne Tie & Maarten IJzerman

  5. Department of Medical Oncology, Cabrini Health, Melbourne, VIC, Australia

    Jeremy Shapiro

  6. Department of Medical Oncology, Western Health, Melbourne, VIC, Australia

    Suzanne Kosmider, Jeanne Tie & Peter Gibbs

  7. Department of Medical Oncology, Eastern Health, Melbourne, VIC, Australia

    Rachel Wong

  8. Eastern Health Clinical School, Monash University, Box Hill, VIC, Australia

    Rachel Wong

  9. Department of Medical Oncology, Northern Health, Melbourne, VIC, Australia

    Belinda Lee

  10. Department of Medical Oncology, Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia

    Matthew Burge

  11. Department of Medical Oncology, The Canberra Hospital, Canberra, ACT, Australia

    Desmond Yip

  12. Department of Medical Oncology, Royal Hobart Hospital, Hobart, TAS, Australia

    Louise Nott

  13. Department of Medical Oncology, Fiona Stanley Hospital, Perth, WA, Australia

    Adnan Khattak

  14. Department of Medical Oncology, Campbelltown Hospital, Campbelltown, NSW, Australia

    Stephanie Lim

  15. Department of Medical Oncology, Gold Coast University Hospital, Gold Coast, QLD, Australia

    Susan Caird

Authors
  1. Koen Degeling
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  2. Hui-Li Wong
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  3. Hendrik Koffijberg
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  4. Azim Jalali
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  5. Jeremy Shapiro
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  15. Susan Caird
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  16. Peter Gibbs
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  17. Maarten IJzerman
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Contributions

KD, HLW, HK, PG, and MIJ contributed to the study conception and design. KD analyzed the data in close collaboration with HLW and under supervision of PG and MIJ. KD developed the simulation under supervision of HK and MIJ. All authors contributed to the interpretation and discussion of the results. The first draft of the manuscript was prepared by KD and HLW, and critically reviewed by all other authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Koen Degeling.

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Funding

This research was partly funded by the Netherlands Organisation for Health Research and Development (ZonMW) as part of the Translational Research Program (project number: 446001006). Roche Products Pty Limited (Australia) provided financial assistance for the development, installation and maintenance of the TRACC database. BioGrid Australia manages the TRACC database and provided data access and support.

Conflict of interest

MB has served on an advisory board to Roche, the manufacturer of bevacizumab. The authors declare that there is no conflict of interest regarding the publication of this article.

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Degeling, K., Wong, HL., Koffijberg, H. et al. 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, 1263–1275 (2020). https://doi.org/10.1007/s40273-020-00951-1

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