Variance estimation for medical decision analysis

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Abstract
We have derived the variance of an expected utility for a probability tree in medical decision analysis based on a Taylor series approximation of the expected utility as a function of the probability and utility values used in the decision tree. The resulting variance estimate is an algebraic expression of the variances associated with the probability and utility estimates used. We also derive expressions for the case where the input parameter estimates are not independent. We discuss the choice of input parameters and their variance estimates and give an example that compares two protocols for the treatment of chlamydial infection.