Probabilistic Sensitivity Analysis Using Monte Carlo Simulation

TLDR

Medical decision analyses often rely on unreliable data, and traditional sensitivity analysis becomes cumbersome when more than two parameters vary simultaneously. This paper proposes a practical probabilistic sensitivity analysis that simultaneously considers uncertainties in all model parameters. The authors assume each probability and utility follows a distribution defined by a baseline estimate and a 95 % confidence bound, then perform Monte Carlo simulations of the decision tree, recording mean expected utility, standard deviation, optimal strategy frequency, and utility trade‑off frequencies. Applied to a previously published decision analysis, the technique proved easy to use and yields valuable insights, making it a useful addition to decision analysts’ toolkit.

Abstract

The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying 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 probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability 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 assigned 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 "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated 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.