Incremental Cost-Effectiveness Statistical Inference: Calculations and Communications

We illustrate use of nonparametric statistical methods to compare alternative treatments for a particular disease or condition on both their relative effectiveness and their relative cost. These Incremental Cost Effectiveness (ICE) methods are based upon Bootstrapping, i.e. Resampling with Replacement from observational or clinical-trial data on individual patients. We first show how a reasonable numerical value for the “Shadow Price of Health” can be chosen using functions within the ICEinfer R-package when effectiveness is not measured in "QALY"s. We also argue that simple histograms are ideal for communicating key findings to regulators, while our more detailed graphics may well be more informative and compelling for other health-care stakeholders.