IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v17y1997i3p263-275.html
   My bibliography  Save this article

Use of Influence Diagrams to Structure Medical Decisions

Author

Listed:
  • Robert F. Nease JR
  • Douglas K. Owens

Abstract

Influence diagrams are compact representations of decision problems that are mathematically equivalent to decision trees. The authors present five important principles for structuring a decision as an influence diagram: 1) start at the value node and work back to the decision nodes; 2) draw the arcs in the direction that makes the probabilities easiest to assess; 3) use informational arcs to specify which events will have been observed at the time each decision is made; 4) ensure that missing arcs reflect intentional assertions about conditional independence and the timing of observations; and 5) ensure that there are no cycles in the influence diagram. They then build an influence diagram for the problem of staging non-small-cell lung cancer as an illustration. Influence diagrams offer several strengths for structuring medical decisions. They represent graphically and compactly the probabilistic relationships between parameters in the model. Influence diagrams also allow the model to be structured in a fashion that eases the necessary probability assessments, regardless of whether the assessments are based on available evidence or on expert judgment. Influence diagrams provide an important complement to decision trees, especially for representing probabilistic relationships among variables in a decision model. Key words : decision analysis; influence diagrams; decision tree; decision techniques; cost-effectiveness analysis. (Med Decis Making 1997;17:263-275)

Suggested Citation

  • Robert F. Nease JR & Douglas K. Owens, 1997. "Use of Influence Diagrams to Structure Medical Decisions," Medical Decision Making, , vol. 17(3), pages 263-275, July.
    • Handle: RePEc:sae:medema:v:17:y:1997:i:3:p:263-275
    DOI: 10.1177/0272989X9701700302
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X9701700302
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X9701700302?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. David J. Malenka & Gene L. Colice & Charles Jacobs & J. Robert Beck, 1989. "Mediastinal Staging in Non-small-cell Lung Cancer," Medical Decision Making, , vol. 9(4), pages 231-242, December.
    2. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    2. Stephen G. Pauker & John B. Wong, 2005. "The Influence of Influence Diagrams in Medicine," Decision Analysis, INFORMS, vol. 2(4), pages 238-244, December.
    3. Dy, Sydney Morss & Rubin, Haya R. & Lehmann, Harold P., 2005. "Why do patients and families request transfers to tertiary care? a qualitative study," Social Science & Medicine, Elsevier, vol. 61(8), pages 1846-1853, October.
    4. Mark Helfand & Stephen G. Pauker, 1997. "Influence Diagrams:," Medical Decision Making, , vol. 17(3), pages 351-352, July.
    5. Douglas K. Owens, 2002. "Analytic Tools for Public Health Decision Making," Medical Decision Making, , vol. 22(1_suppl), pages 3-10, September.

    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. repec:cup:judgdm:v:1:y:2006:i::p:162-173 is not listed on IDEAS
    2. Fernandez del Pozo, J. A. & Bielza, C. & Gomez, M., 2005. "A list-based compact representation for large decision tables management," European Journal of Operational Research, Elsevier, vol. 160(3), pages 638-662, February.
    3. Els Hannes & Diana Kusumastuti & Maikel Espinosa & Davy Janssens & Koen Vanhoof & Geert Wets, 2012. "Mental maps and travel behaviour: meanings and models," Journal of Geographical Systems, Springer, vol. 14(2), pages 143-165, April.
    4. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    5. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2010. "Bayes geometric scaling model for common cause failure rates," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 70-76.
    6. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    7. Robert F. Nease JR, 1996. "Do Violations of the Axioms of Expected Utility Theory Threaten Decision Analysis?," Medical Decision Making, , vol. 16(4), pages 399-403, October.
    8. Prakash Shenoy, 1998. "Game Trees For Decision Analysis," Theory and Decision, Springer, vol. 44(2), pages 149-171, April.
    9. Lopez-Diaz, Miguel & Rodriguez-Muniz, Luis J., 2007. "Influence diagrams with super value nodes involving imprecise information," European Journal of Operational Research, Elsevier, vol. 179(1), pages 203-219, May.
    10. Oepping, Hardy, 2016. "Ein Bayes-Netz zur Analyse des Absturzrisikos im Gerüstbau [A Bayesian network for analysing the risk of falling from height in scaffolding]," MPRA Paper 73602, University Library of Munich, Germany.
    11. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    12. David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    13. Borgonovo, Emanuele & Tonoli, Fabio, 2014. "Decision-network polynomials and the sensitivity of decision-support models," European Journal of Operational Research, Elsevier, vol. 239(2), pages 490-503.
    14. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    15. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    16. Vic Hasselblad & Douglas C. McCrory, 1995. "Meta-analytic Tools for Medical Decision Making: A Practical Guide," Medical Decision Making, , vol. 15(1), pages 81-96, February.
    17. Concha Bielza & Prakash P. Shenoy, 1999. "A Comparison of Graphical Techniques for Asymmetric Decision Problems," Management Science, INFORMS, vol. 45(11), pages 1552-1569, November.
    18. Thwaites, Peter A. & Smith, Jim Q., 2018. "A graphical method for simplifying Bayesian games," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 3-11.
    19. Stephen G. Pauker & John B. Wong, 2005. "The Influence of Influence Diagrams in Medicine," Decision Analysis, INFORMS, vol. 2(4), pages 238-244, December.
    20. John M. Charnes & Prakash P. Shenoy, 2004. "Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation," Management Science, INFORMS, vol. 50(3), pages 405-418, March.
    21. Barry R. Cobb, 2007. "Influence Diagrams with Continuous Decision Variables and Non-Gaussian Uncertainties," Decision Analysis, INFORMS, vol. 4(3), pages 136-155, September.

    More about this item

    Statistics

    Access and download statistics

    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:17:y:1997:i:3:p:263-275. 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.