Probabilistic analysis of decision trees using symbolic algebra

Abstract

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.