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.

翻译：我们阐述了使用非参数统计方法比较特定疾病或状况下不同治疗方案在相对有效性和相对成本上的差异。这些增量成本-效果（ICE）方法基于自助法（Bootstrapping），即从个体患者的观察性或临床试验数据中进行有放回重抽样。我们首先展示了当有效性不以“质量调整生命年（QALY）”衡量时，如何利用ICEinfer R包中的函数选择合理的“健康影子价格”数值。此外，我们主张简单的直方图最适合向监管机构传达关键发现，而我们的更详细图形对其他医疗保健利益相关者可能更具信息量和说服力。