School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
Health Econ. 2021 Aug;30(8):1818-1832. doi: 10.1002/hec.4263. Epub 2021 May 4.
We develop a method for data-driven estimation and analysis of heterogeneity in cost-effectiveness analyses (CEA) with experimental or observational individual-level data. Our implementation uses causal forests and cross-fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual-level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost-effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data.
我们开发了一种方法，用于从实验或观察性个体水平数据中对成本效益分析（CEA）中的异质性进行数据驱动的估计和分析。我们的实现使用因果森林和交叉拟合增强逆概率加权学习来估计增量结果、成本和净货币收益以及与 CEA 相关的其他参数的异质性。我们还展示了如何以与 CEA 异质性分析相关的方式对结果进行可视化，例如使用个体水平的成本效益平面。使用模拟数据集和实现我们方法的 R 包，我们展示了如何使用该方法来估计整个样本或子群体中的平均成本效益，探索和分析增量结果、成本和净货币收益（及其决定因素）的异质性，并从数据中学习政策规则。
