Probabilistic analysis of decision trees using Monte Carlo simulation

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 techniques may be applied to complex decision models.