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Stochastic-Tree Models in Medical Decision Making

Author

Listed:
  • Gordon B. Hazen

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119)

  • James M. Pellissier

    (Clinical and Health Economic Statistics, Merck Research Laboratories, 10 Sentry Parkway, BL3-2, Blue Bell, Pennsylvania 19422)

  • Jayavel Sounderpandian

    (Department of Business, University of Wisconsin-Parkside, Box 2000, Kenosha, Wisconsin 53141-2000)

Abstract

The stochastic tree is a recently introduced generalization of the decision tree which allows the explicit depiction of temporal uncertainty while still employing the familiar rollback procedure for decision trees. We offer an introduction to stochastic-tree modeling and techniques involved in their application to medical-treatment decisions. We also describe an application of these tools to the analysis of the decision to undergo a total hip replacement from the perspectives of an individual patient (via utility analysis) and of society (via cost-effectiveness analysis).

Suggested Citation

  • Gordon B. Hazen & James M. Pellissier & Jayavel Sounderpandian, 1998. "Stochastic-Tree Models in Medical Decision Making," Interfaces, INFORMS, vol. 28(4), pages 64-80, August.
    • Handle: RePEc:inm:orinte:v:28:y:1998:i:4:p:64-80
    DOI: 10.1287/inte.28.4.64
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    References listed on IDEAS

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    1. Dennis A. Plante & Jay F. Piccirillo & Robert A. Sofferman, 1987. "Decision Analysis of Treatment Options in Pyriform Sinus Carcinoma," Medical Decision Making, , vol. 7(2), pages 74-83, June.
    2. Pellissier, James M. & Hazen, Gordon B., 1994. "Implementation of continuous risk utility assessment: The total hip replacement decision," Socio-Economic Planning Sciences, Elsevier, vol. 28(4), pages 251-276, December.
    3. Cathleen Mooney & Alvin I. Mushlin & Charles E. Phelps, 1990. "Targeting Assessments of Magnetic Resonance Imaging in suspected Multiple sclerosis," Medical Decision Making, , vol. 10(2), pages 77-94, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Gordon Hazen, 2000. "Preference Factoring for Stochastic Trees," Management Science, INFORMS, vol. 46(3), pages 389-403, March.
    2. Nicky J. Welton & A. E. Ades, 2005. "Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration," Medical Decision Making, , vol. 25(6), pages 633-645, November.
    3. C. Armero & G. García‐Donato & A. López‐Quílez, 2010. "Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643, June.
    4. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    5. Donald L. Keefer & Craig W. Kirkwood & James L. Corner, 2004. "Perspective on Decision Analysis Applications, 1990–2001," Decision Analysis, INFORMS, vol. 1(1), pages 4-22, March.
    6. 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.

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