The data for medical decision analyses are often unreliable. Traditional sensitivity analysisvarying one or more probability or utility estimates from baseline values to see if the optimal strategy changes -is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilis- tic sensitivity analysis, in which uncertainties in all values are considered simultane- ously. The uncertainty in each probability and utility is assumed to possess a proba- bility distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly as- signed a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy &dquo;buys&dquo; or &dquo;costs&dquo; a specified amount of utility relative to the remaining strategies. As illustrat- ed by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst. (Med Decis Making 5: 157-177, 1985)