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Incorporating Biological Natural History in Simulation Models: Empirical Estimates of the Progression of End-Stage Liver Disease

Author

Listed:
  • Oguzhan Alagoz

    (Department of Industrial and Systems Engineering, University of Wisconsin, Madison)

  • Cindy L. Bryce

    (Section of Decision Sciences and Clinical Systems Modeling, Division of General Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Center for Research on Health Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA)

  • Steven Shechter

    (Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA)

  • Andrew Schaefer

    (Section of Decision Sciences and Clinical Systems Modeling, Division of General Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA)

  • Chung-Chou H. Chang

    (Center for Research on Health Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA)

  • Derek C. Angus

    (Section of Decision Sciences and Clinical Systems Modeling, Division of General Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Center for Research on Health Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, The CRISMA Laboratory, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA)

  • Mark S. Roberts

    (Section of Decision Sciences and Clinical Systems Modeling, Division of General Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Center for Research on Health Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, robertsm@upmc.edu)

Abstract

Objective . To develop an empiric natural-history model that can predict quantitative changes in the laboratory values and clinical characteristics of patients with end-stage liver disease (ESLD), to be used to calibrate an individual microsimulation model. Methods . The authors report the development of a stochastic model that uses cubic splines to interpolate between observed laboratory values over time in a cohort of 1997 patients with ESLD awaiting liver transplantation at the University of Pittsburgh Medical Center. The splines were recursively sampled to provide a stochastic, quantitative natural history of each candidate’s disease. Results . The model was able to simulate the types of erratic disease trajectories that occur in individual patients and was able to preserve the statistical properties of the natural history of ESLD in cohorts of real patients. Moreover, the model was able to predict pretransplant survival rates (87% at 1 year), which were statistically similar to rates observed in the authors’ local cohort (92%). Conclusions . Cubic splines can be used to generate quantitative natural histories for individual patients with ESLD and may be useful for developing clinically robust microsimulation models of other diseases.

Suggested Citation

  • Oguzhan Alagoz & Cindy L. Bryce & Steven Shechter & Andrew Schaefer & Chung-Chou H. Chang & Derek C. Angus & Mark S. Roberts, 2005. "Incorporating Biological Natural History in Simulation Models: Empirical Estimates of the Progression of End-Stage Liver Disease," Medical Decision Making, , vol. 25(6), pages 620-632, November.
    • Handle: RePEc:sae:medema:v:25:y:2005:i:6:p:620-632
    DOI: 10.1177/0272989X05282719
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    Citations

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    Cited by:

    1. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List," Operations Research, INFORMS, vol. 55(1), pages 24-36, February.
    2. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
    3. Oguzhan Alagoz & Heather Hsu & Andrew J. Schaefer & Mark S. Roberts, 2010. "Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty," Medical Decision Making, , vol. 30(4), pages 474-483, July.
    4. Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.
    5. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
    6. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Oguzhan Alagoz & Mark S. Roberts, 2008. "Estimating the Patient's Price of Privacy in Liver Transplantation," Operations Research, INFORMS, vol. 56(6), pages 1393-1410, December.
    7. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    8. Zeynep Erkin & Matthew D. Bailey & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2010. "Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach," Decision Analysis, INFORMS, vol. 7(4), pages 358-365, December.
    9. 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.
    10. Sepehr Nemati & Zeynep G. Icten & Lisa M. Maillart & Andrew J. Schaefer, 2020. "Mitigating Information Asymmetry in Liver Allocation," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 234-248, April.
    11. Alireza Boloori & Soroush Saghafian & Harini A. Chakkera & Curtiss B. Cook, 2020. "Data-Driven Management of Post-transplant Medications: An Ambiguous Partially Observable Markov Decision Process Approach," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1066-1087, September.
    12. Karen T. Hicklin & Julie S. Ivy & James R. Wilson & Fay Cobb Payton & Meera Viswanathan & Evan R. Myers, 2019. "Simulation model of the relationship between cesarean section rates and labor duration," Health Care Management Science, Springer, vol. 22(4), pages 635-657, December.
    13. Raffaele Argiento & Alessandra Guglielmi & Ettore Lanzarone & Inad Nawajah, 2016. "A Bayesian framework for describing and predicting the stochastic demand of home care patients," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 254-279, June.
    14. Natasha Stout & Sue Goldie, 2008. "Keeping the noise down: common random numbers for disease simulation modeling," Health Care Management Science, Springer, vol. 11(4), pages 399-406, December.

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