IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v43y2023i7-8p949-960.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X231201621
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X231201621?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carmen María Yago & Francisco Javier Díez, 2023. "DESnets: A Graphical Representation for Discrete Event Simulation and Cost-Effectiveness Analysis," Mathematics, MDPI, vol. 11(7), pages 1-24, March.
    2. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.
    3. Bernhard Ultsch & Oliver Damm & Philippe Beutels & Joke Bilcke & Bernd Brüggenjürgen & Andreas Gerber-Grote & Wolfgang Greiner & Germaine Hanquet & Raymond Hutubessy & Mark Jit & Mirjam Knol & Rüdiger, 2016. "Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community," PharmacoEconomics, Springer, vol. 34(3), pages 227-244, March.
    4. Becky Pennington & Alex Filby & Lesley Owen & Matthew Taylor, 2018. "Smoking Cessation: A Comparison of Two Model Structures," PharmacoEconomics, Springer, vol. 36(9), pages 1101-1112, September.
    5. Peter J. Dodd & Jeff J. Pennington & Liza Bronner Murrison & David W. Dowdy, 2018. "Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation," Medical Decision Making, , vol. 38(8), pages 930-941, November.
    6. Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    7. Isaac Corro Ramos & Talitha Feenstra & Salah Ghabri & Maiwenn Al, 2024. "Evaluating the Validation Process: Embracing Complexity and Transparency in Health Economic Modelling," PharmacoEconomics, Springer, vol. 42(7), pages 715-719, July.
    8. Stuart J. Wright & William G. Newman & Katherine Payne, 2019. "Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review," PharmacoEconomics, Springer, vol. 37(8), pages 1011-1027, August.
    9. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    10. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    11. Jen Kruger & Daniel Pollard & Hasan Basarir & Praveen Thokala & Debbie Cooke & Marie Clark & Rod Bond & Simon Heller & Alan Brennan, 2015. "Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models," Medical Decision Making, , vol. 35(7), pages 872-887, October.
    12. Sarah Bates & Thomas Bayley & Paul Norman & Penny Breeze & Alan Brennan, 2020. "A Systematic Review of Methods to Predict Weight Trajectories in Health Economic Models of Behavioral Weight-Management Programs: The Potential Role of Psychosocial Factors," Medical Decision Making, , vol. 40(1), pages 90-105, January.
    13. F. Tomini & F. Prinzen & A. D. I. Asselt, 2016. "A review of economic evaluation models for cardiac resynchronization therapy with implantable cardioverter defibrillators in patients with heart failure," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1159-1172, December.
    14. Luis Hernandez & Asli Ozen & Rodrigo DosSantos & Denis Getsios, 2016. "Systematic Review of Model-Based Economic Evaluations of Treatments for Alzheimer’s Disease," PharmacoEconomics, Springer, vol. 34(7), pages 681-707, July.
    15. Sarang Deo & Sameer Mehta & Charles J. Corbett, 2022. "Optimal Scale‐Up of HIV Treatment Programs in Resource‐Limited Settings Under Supply Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 883-905, March.
    16. Matthew J. Glover & Edmund Jones & Katya L. Masconi & Michael J. Sweeting & Simon G. Thompson, 2018. "Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening," Medical Decision Making, , vol. 38(4), pages 439-451, May.
    17. Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.
    18. Paul Crosland & Deborah A. Marshall & Seyed Hossein Hosseini & Nicholas Ho & Catherine Vacher & Adam Skinner & Kim-Huong Nguyen & Frank Iorfino & Sebastian Rosenberg & Yun Ju Christine Song & Apostolo, 2024. "Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning," PharmacoEconomics, Springer, vol. 42(12), pages 1301-1315, December.
    19. Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.
    20. L. B. Standfield & T. A. Comans & P. A. Scuffham, 2017. "An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(1), pages 33-47, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:43:y:2023:i:7-8:p:949-960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.