Uncertainty in decision models analyzing cost-effectiveness: The joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method

Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous transluminal angioplasty with bypass surgery for femoropopliteal lesions. Each probability in the model was simulated with a first-order Monte Carte simulation to represent sampling uncertainty. Superimposed on this, a second-order Monte Carlo simulation was performed to represent parameter uncertainty, drawing the probability values from nonparametric distributions based on published data or from primary collected data as available. After simulation of a mixed (i.e., non-identical) cohort of 30,000 patients, 3,000 bootstrap samples of 1,000 patients each were drawn and the joint distribution of mean incremental costs and mean effectiveness gained was evaluated. Results. Using a bootstrap sample size of 1,000 patients, 92.7% of the joint distribution of mean incremental costs and mean effectiveness gained fell in the quadrant where angioplasty dominated bypass surgery. Another 6.9% of samples demonstrated either greater effectiveness with an incremental cost-effectiveness ratio of at most $20,000/QALY gained, or cost savings with a ratio of at least $20,000 saved/QALY lost. Conclusion. A nonparametric bootstrap method can be used to estimate the joint distribution of mean incremental costs and mean effectiveness gained, and the results can provide an understanding of the uncertainty in a cost-effectiveness analysis based on a decision model.