This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Willard, K. E. Articles by Critchfield, G. C. Search for Related Content PubMed PubMed Citation Articles by Willard, K. E. Articles by Critchfield, G. C. Social Bookmarking                   What's this?

Probabilistic Analysis of Decision Trees Using Symbolic Algebra

Gregory C. Critchfield, MD

Uncertainty in medical decision making techniques occurs in the specification of both decision tree probabilities and utilities. Using a computer-based algebraic approach, methods for modeling this uncertainty have been formulated. This analytic procedure allows an exact calculation of the statistical variance at the final decision node using automated symbolic manipulation. Confidence and conditional confidence levels for the preferred decision are derived from gaussian theory, and the mutual information index that identifies probabilistically important tree variables is provided. The computer-based algebraic method is illustrated for a problem previously analyzed by Monte Carlo simulation. This methodology provides the decision analyst with a procedure to evaluate the outcome of specification uncertainty, in many decision problems, without resorting to Monte Carlo analysis. Key words: decision analysis; sensitivity analysis; statistical analysis; symbolic algebra; statistical confidence; information theory. (Med Decis Making 6:93-100, 1986)

Medical Decision Making, Vol. 6, No. 2, 93-100 (1986)
DOI: 10.1177/0272989X8600600206


CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				A. P. Johnson-Masotti, P. W. Laud, R. G. Hoffmann, M. J. Hayat, and S. D. Pinkerton A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention Med Decis Making, November 1, 2004; 24(6): 634 - 653. [Abstract] [PDF]

				M. Krahn, J. B. Wong, J. Heathcote, L. Scully, and L. Seeff Estimating the Prognosis of Hepatitis C Patients Infected by Transfusion in Canada between 1986 and 1990 Med Decis Making, January 1, 2004; 24(1): 20 - 29. [Abstract] [PDF]

				A. P. Johnson-Masotti, P. W. Laud, R. G. Hoffmann, M. J. Hayat, and S. D. Pinkerton Probabilistic Cost-Effectiveness Analysis of HIV Prevention: Comparing a Bayesian Approach With Traditional Deterministic Sensitivity Analysis Eval Rev, August 1, 2001; 25(4): 474 - 502. [Abstract] [PDF]

				M. D. Krahn, G. Naglie, D. Naimark, D. A. Redelmeier, and A. S. Detsky Primer on Medical Decision Analysis: Part 4-Analyzing the Model and Interpreting the Results Med Decis Making, April 1, 1997; 17(2): 142 - 151. [Abstract] [PDF]

				J. F. Merz, M. J. Small, and P. S. Fischbeck Measuring Decision Sensitivity: A Combined Monte Carlo - Logistic Regression Approach Med Decis Making, August 1, 1992; 12(3): 189 - 196. [Abstract] [PDF]

				J. R. Beck Independent Development of Probabilistic Sensitivity Analysis Med Decis Making, June 1, 1986; 6(2): 66 - 67. [PDF]