In this latest update we highlight a recent International Society of Pharmacoeconomics and Outcomes Research Good Practice Report on machine learning (ML) for health economics and outcomes research. We specifically discuss use cases of ML that offer opportunities in the generation of evidence using real-world data, including improvements in the identification of study cohorts, confounder identification and adjustment and estimating treatment effect heterogeneity. Barriers to the wider adoption of ML methods are also discussed.
Keywords: HTA; ISPOR; International Society of Pharmacoeconomics and Outcomes Research; RWE; comparative effectiveness research; health technology assessment; machine learning; real-world evidence.