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A Simulation Model to Estimate the Cost and Effectiveness of Alternative Dialysis Initiation Strategies

Author

Listed:
  • Chris P. Lee

    (Operations and Information Management Department, The Wharton School, University of Pennsylvania, PA)

  • Glenn M. Chertow

    (Division of Nephrology, Department of Medicine, University of California San Francisco, CA)

  • Stefanos A. Zenios

    (Graduate School of Business, Stanford University, CA, stefzen@stanford.edu)

Abstract

Background . Patients with end-stage renal disease (ESRD) require dialysis to maintain survival. The optimal timing of dialysis initiation in terms of cost-effectiveness has not been established. Methods . We developed a simulation model of individuals progressing towards ESRD and requiring dialysis. It can be used to analyze dialysis strategies and scenarios. It was embedded in an optimization frame worked to derive improved strategies. Results . Actual (historical) and simulated survival curves and hospitalization rates were virtually indistinguishable. The model overestimated transplantation costs (10%) but it was related to confounding by Medicare coverage. To assess the model's robustness, we examined several dialysis strategies while input parameters were perturbed. Under all 38 scenarios, relative rankings remained unchanged. An improved policy for a hypothetical patient was derived using an optimization algorithm. Conclusion . The model produces reliable results and is robust. It enables the cost-effetiveness analysis of dialysis strategies.

Suggested Citation

  • Chris P. Lee & Glenn M. Chertow & Stefanos A. Zenios, 2006. "A Simulation Model to Estimate the Cost and Effectiveness of Alternative Dialysis Initiation Strategies," Medical Decision Making, , vol. 26(5), pages 535-549, September.
  • Handle: RePEc:sae:medema:v:26:y:2006:i:5:p:535-549
    DOI: 10.1177/0272989X06290488
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    Citations

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    Cited by:

    1. Chris P. Lee & Glenn M. Chertow & Stefanos A. Zenios, 2008. "Optimal Initiation and Management of Dialysis Therapy," Operations Research, INFORMS, vol. 56(6), pages 1428-1449, December.
    2. Anne-Line Couillerot-Peyrondet & Cléa Sambuc & Yoël Sainsaulieu & Cécile Couchoud & Isabelle Bongiovanni-Delarozière, 2017. "A comprehensive approach to assess the costs of renal replacement therapy for end-stage renal disease in France: the importance of age, diabetes status, and clinical events," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(4), pages 459-469, May.
    3. Mohsen Yaghoubi & Sonya Cressman & Louisa Edwards & Steven Shechter & Mary M. Doyle-Waters & Paul Keown & Ruth Sapir-Pichhadze & Stirling Bryan, 2023. "A Systematic Review of Kidney Transplantation Decision Modelling Studies," Applied Health Economics and Health Policy, Springer, vol. 21(1), pages 39-51, January.
    4. Zlatana Nenova & Jennifer Shang, 2022. "Chronic Disease Progression Prediction: Leveraging Case‐Based Reasoning and Big Data Analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 259-280, January.
    5. Joseph Menzin & Lisa Lines & Daniel Weiner & Peter Neumann & Christine Nichols & Lauren Rodriguez & Irene Agodoa & Tracy Mayne, 2011. "A Review of the Costs and Cost Effectiveness of Interventions in Chronic Kidney Disease," PharmacoEconomics, Springer, vol. 29(10), pages 839-861, October.
    6. Wenjuan Fan & Yang Zong & Subodha Kumar, 2022. "Optimal treatment of chronic kidney disease with uncertainty in obtaining a transplantable kidney: an MDP based approach," Annals of Operations Research, Springer, vol. 316(1), pages 269-302, September.

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