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Probabilistic Sensitivity Analysis Using Monte Carlo Simulation: A Practical Approach by Peter Doubilet; Colin B. Begg; Milton C. Weinstein; Peter Braun; Barbara J. McNeil is a Economics, Econometrics and Finance article available to read on EtoBox.

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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 strategie

Who reads Probabilistic Sensitivity Analysis Using Monte Carlo Simulation: A Practical Approach?

Author

Peter Doubilet; Colin B. Begg; Milton C. Weinstein; Peter Braun; Barbara J. McNeil

Publisher

SAGE Publications; Sage Science Press (ISSN 0272-989X)

Published

1985

Language

EN

Field

Economics, Econometrics and Finance (Social Sciences)