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Probabilistic Analysis of Decision Trees Using Monte Carlo Simulation

Author

Listed:
  • Gregory C. Critchfield
  • Keith E. Willard

Abstract

The authors describe methods for modeling uncertainty in the specification of decision tree probabilities and utilities using Monte Carlo simulation techniques. Exact confidence levels based upon the underlying probabilistic structure are provided. Probabilistic measures of sensitivity are derived in terms of classical information theory. These measures identify which variables are probabilistically important components of the decision. These techniques are illustrated in terms of the clinical problem of anticoagulation versus observation in the setting of deep vein thrombosis during the first trimester of pregnancy. These methods provide the decision analyst with powerful yet simple tools which give quantitative insight into the structure and inherent limitations of decision models arising from specification uncertainty. The tech niques may be applied to complex decision models. Key words: decision analysis; sensitivity analysis; statistical analysis; statistical confidence; information theory. (Med Decis Making 6:85-92, 1986)

Suggested Citation

  • Gregory C. Critchfield & Keith E. Willard, 1986. "Probabilistic Analysis of Decision Trees Using Monte Carlo Simulation," Medical Decision Making, , vol. 6(2), pages 85-92, June.
    • Handle: RePEc:sae:medema:v:6:y:1986:i:2:p:85-92
    DOI: 10.1177/0272989X8600600205
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    References listed on IDEAS

    as
    1. Karen Klein & Stephen G. Pauker, 1981. "Recurrent Deep Venous Thrombosis in Pregnancy," Medical Decision Making, , vol. 1(2), pages 181-202, June.
    2. Robert D. Rifkin, 1983. "Statistical Considerations in Medical Decision Models," Medical Decision Making, , vol. 3(2), pages 197-214, June.
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