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Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to Metastatic Prostate Cancer

Author

Listed:
  • Elizabeth A. Handorf

    (Rutgers University School of Public Health, Cancer Institute of New Jersey, USA)

  • J. Robert Beck

    (Fox Chase Cancer Center, Cancer Prevention and Control Program, Philadelphia, PA, USA)

  • Andres Correa

    (Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA)

  • Chethan Ramamurthy

    (Division Hematology/Oncology, Mays Cancer Center UT Health San Antonio, San Antonio, TX, USA)

  • Daniel M. Geynisman

    (Department of Hematology/Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA)

Abstract

Purpose Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. We developed a general microsimulation framework to study therapy sequence and applied it to metastatic prostate cancer. Methods We constructed a discrete-time state transition model to study 2 lines of therapy. Using digitized published survival curves (progression-free survival, time to progression, and overall survival [OS]), we inferred event types (progression or death) and estimated transition probabilities using cumulative incidence functions with competing risks. We incorporated within-patient dependence over time; first-line therapy response informed subsequent event probabilities. Parameters governing within-patient dependence calibrated the model-based results to a target clinical trial. We applied these methods to 2 therapy sequences for metastatic prostate cancer, wherein both docetaxel (DCT) and abiraterone acetate (AA) are appropriate for either first- or second-line treatment. We assessed costs and quality-adjusted life-years (5-y QALYs) for 2 treatment strategies: DCT → AA versus AA → DCT. Results Models assuming within-patient independence overestimated OS time, which corrected with the calibration approach. With generic pricing, AA → DCT dominated DCT → AA, (higher 5-y QALYs and lower costs), consistent for all values of calibration parameters (including no correction). Model calibration increased the difference in 5-y QALYs between treatment strategies (0.07 uncorrected v. 0.15 with base-case correction). Applying the correction decreased the estimated difference in cost (−$5,360 uncorrected v. −$3,066 corrected). Results were strongly affected by the cost of AA. Under a lifetime horizon, AA → DCT was no longer dominant but still cost-effective (incremental cost-effectiveness ratio: $19,463). Conclusions We demonstrate a microsimulation approach to study the cost-effectiveness of therapy sequences for advanced prostate cancer, taking care to account for within-patient dependence. Highlights We developed a discrete-time state transition model for studying therapy sequence in advanced cancers. Results are sensitive to dependence within patients. A calibration approach can introduce dependence across lines of therapy and closely match simulation outcomes to target trial outcomes.

Suggested Citation

  • Elizabeth A. Handorf & J. Robert Beck & Andres Correa & Chethan Ramamurthy & Daniel M. Geynisman, 2023. "Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to Metastatic Prostate Cancer," Medical Decision Making, , vol. 43(7-8), pages 949-960, October.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:7-8:p:949-960
    DOI: 10.1177/0272989X231201621
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    References listed on IDEAS

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    1. Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Hawre J. Jalal & M. G. Myriam Hunink & Petros Pechlivanoglou, 2018. "Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial," Medical Decision Making, , vol. 38(3), pages 400-422, April.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    3. John Graves & Shawn Garbett & Zilu Zhou & Jonathan S. Schildcrout & Josh Peterson, 2021. "Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation," Medical Decision Making, , vol. 41(4), pages 453-464, May.
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