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：本文阐述如何使用非参数统计方法，基于相对有效性和相对成本比较针对特定疾病或状况的替代治疗方案。这些增量成本效果（ICE）方法基于自助法（Bootstrapping），即对患者个体观察数据或临床试验数据进行有放回重抽样。我们首先展示当有效性不以“QALY”（质量调整生命年）衡量时，如何利用ICEinfer R包中的函数选择合适的“健康影子价格”数值。此外，我们论证简单直方图是向监管机构传达关键结果的理想工具，而针对其他医疗保健利益相关者，我们更详细的图形可能更具信息量和说服力。