Related Experiment Video
Updated: Jul 10, 2026
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
Published on: September 10, 2018
Uncertainty in decision models analyzing cost-effectiveness: the joint distribution of incremental costs and
M G Hunink1, J R Bult, J de Vries
1Department of Health Sciences, University of Groningen, The Netherlands. hunink@epib.fgg.eur.nl
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 Carlo 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.
Related Concept Videos
01:09Decision Making: P-value Method
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:24Bootstrapping
00:54Uncertainty: Confidence Intervals
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:14Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...