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Gaussian Process Modeling in Conjunction with Individual Patient Simulation Modeling: A Case Study Describing the Calculation of Cost-Effectiveness Ratios for the Treatment of Established Osteoporosis

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  • M. D. Stevenson
  • J. Oakley
  • J. B. Chilcott

Abstract

Individual patient-level models can simulate more complex disease processes than cohort-based approaches. However, large numbers of patients need to be simulated to reduce 1storder uncertainty, increasing the computational time required and often resulting in the inability to perform extensive sensitivity analyses. A solution, employing Gaussian process techniques, is presented using a case study, evaluating the cost-effectiveness of a sample of treatments for established osteoporosis. The Gaussian process model accurately formulated a statistical relationship between the inputs to the individual patient model and its outputs. This model reducedthe time required for future runs from 150 min to virtually-instantaneous, allowing probabilistic sensitivity analyses-to be undertaken. This reduction in computational time was achieved with minimal loss in accuracy. The authors believe that this case study demonstrates the value of this technique in handling 1st- and 2nd-order uncertainty in the context of health economic modeling, particularly when more widely used techniques are computationally expensive or are unable to accurately model patient histories.

Suggested Citation

  • M. D. Stevenson & J. Oakley & J. B. Chilcott, 2004. "Gaussian Process Modeling in Conjunction with Individual Patient Simulation Modeling: A Case Study Describing the Calculation of Cost-Effectiveness Ratios for the Treatment of Established Osteoporosis," Medical Decision Making, , vol. 24(1), pages 89-100, January.
  • Handle: RePEc:sae:medema:v:24:y:2004:i:1:p:89-100
    DOI: 10.1177/0272989X03261561
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    Citations

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    Cited by:

    1. Hawre Jalal & Bryan Dowd & François Sainfort & Karen M. Kuntz, 2013. "Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results," Medical Decision Making, , vol. 33(7), pages 880-890, October.
    2. Joke Bilcke & Philippe Beutels & Marc Brisson & Mark Jit, 2011. "Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models," Medical Decision Making, , vol. 31(4), pages 675-692, July.
    3. Jason Madan & Anthony E. Ades & Malcolm Price & Kathryn Maitland & Julie Jemutai & Paul Revill & Nicky J. Welton, 2014. "Strategies for Efficient Computation of the Expected Value of Partial Perfect Information," Medical Decision Making, , vol. 34(3), pages 327-342, April.
    4. Marta O. Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    5. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    6. Aditya Sai & Carolina Vivas-Valencia & Thomas F. Imperiale & Nan Kong, 2019. "Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes," Medical Decision Making, , vol. 39(5), pages 540-552, July.
    7. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.
    8. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    9. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    10. Afschin Gandjour, 2010. "Investment in quality improvement: how to maximize the return," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 31-42, January.
    11. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    12. Ji-Hee Youn & Matt D. Stevenson & Praveen Thokala & Katherine Payne & Maria Goddard, 2019. "Modeling the Economic Impact of Interventions for Older Populations with Multimorbidity: A Method of Linking Multiple Single-Disease Models," Medical Decision Making, , vol. 39(7), pages 842-856, October.
    13. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
    14. Jeremy D. Goldhaber-Fiebert & Hawre J. Jalal, 2016. "Some Health States Are Better Than Others," Medical Decision Making, , vol. 36(8), pages 927-940, November.
    15. Tiago M. de Carvalho & Eveline A. M. Heijnsdijk & Luc Coffeng & Harry J. de Koning, 2019. "Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators," Medical Decision Making, , vol. 39(4), pages 405-413, May.
    16. Bas Groot Koerkamp & Theo Stijnen & Milton C. Weinstein & M. G. Myriam Hunink, 2011. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models," Medical Decision Making, , vol. 31(4), pages 650-661, July.
    17. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.
    18. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.

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