Using causal forests to assess heterogeneity in cost‐effectiveness analysis. (4th May 2021)

• Record Type: Journal Article
• Authors: Bonander, Carl; Svensson, Mikael
• Is Part Of: Health economics. Volume 30:Number 8(2021)
• Page Start: 1818
• Page End: 1832
• Publication Date: 2021-05-04
• Subjects: causal forest -- cost‐effectiveness analysis -- machine learning -- stratified analysis -- treatment heterogeneity; Medical economics -- Periodicals; Economics, Medical -- Periodicals; Health Care Costs -- Periodicals; Health Policy -- economics -- Periodicals; Health Services -- economics -- Periodicals; 362.1
• DOI: 10.1002/hec.4263
• Languages: English
• ISSNs: 1057-9230

Abstract

Abstract: 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.