Nicholas "Nick" Micheletti

MS thesis abstract:

The standard cost-effectiveness acceptability curve is a tool often employed in cost-effectiveness analysis that is calculated by determining one-sided bootstrapped p-values for a test of a hypothesis on incremental net monetary benefits, and mapping them over a range of acceptable resources. With the recent development of the second generation p-value comes an opportunity to innovate upon the often used cost-effectiveness acceptability curve. The second generation acceptability curve is constructed by comparing bootstrapped incremental net monetary benefit confidence intervals to pre-specified null hypothesis intervals. This comparison creates second generation p-values over a range of acceptable resources. We present the process of constructing a second generation acceptability curve and explore methods to depict the curve. The means of interpreting and understanding the second generation acceptability curve are also discussed. Various simulation studies are performed in order to explore the properties of the second generation acceptability curves, from changes to the distribution of costs or effectiveness measures, to other simulation parameters such as like null hypothesis interval width and sample size. We further provide example comparisons of the standard and second generation curves. The second generation curve is also employed in an applied example motivated by a real-world data set from a cancer cost-effectiveness study; specifically we follow a paper that looks at how cancer data sets are handled by cost-effectiveness analysis methods, and how they manage in handling uncertainty and inconclusiveness. Ultimately, the second generation acceptability curve is shown to provide improvements to the standard cost-effectiveness acceptability curve in its ability to depict regions of approximate equivalence and inconclusiveness.