Daniel Polsky (Division of General Internal Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania, USA) Henry A. Glick (Division of General Internal Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania, USA) Richard Willke (Pharmacia and Upjohn, Inc., Kalamazoo, USA) Kevin Schulman (Clinical Economics Research Unit, Division of General Internal Medicine, Georgetown University Medical Center, Washington, DC, USA)
Abstract
We evaluated four methods for computing confidence intervals for cost-effectiveness ratios developed from randomized controlled trials: the box method, the Taylor series method, the nonparametric bootstrap method and the Fieller theorem method. We performed a Monte Carlo experiment to compare these methods. We investigated the relative performance of each method and assessed whether or not it was affected by differing distributions of costs (normal and log normal) and effects (10% absolute difference in mortality resulting from mortality rates of 25% versus 15% in the two groups as well as from mortality rates of 55% versus 45%) or by differing levels of correlation between the costs and effects (correlations of −0.50, −0.25, 0.0, 0.25 and 0.50). The principal criterion used to evaluate the performance of the methods was the probability of miscoverage. Symmetrical miscoverage of the intervals was used as a secondary criterion for evaluating the four methods.
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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.
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