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Decision makers increasingly request evidence on the real-world cost effectiveness of a new treatment. There is, however, a lack of practical guidance on how to conduct an economic evaluation based on registry data and how this evidence can be used in actual decision making. This paper explains the required steps on how to perform a sound economic evaluation using examples from an economic evaluation conducted with real-world data from the Dutch Population based HAematological Registry for Observational Studies. There are three main issues related to using registry data: confounding by indication, missing data, and insufficient numbers of (comparable) patients. If encountered, it is crucial to accurately deal with these issues to maximize the internal validity and generalizability of the outcomes and their value to decision makers. Multivariate regression modeling, propensity score matching, and data synthesis are well-established methods to deal with confounding. Multiple imputation methods should be used in cases where data are missing at random. Furthermore, it is important to base the incremental cost-effectiveness ratio of a new treatment compared with its alternative on comparable groups of (matched) patients, even if matching results in a small analytical population. Unmatched real-world data provide insights into the costs and effects of a treatment in a real-world setting. Decision makers should realize that real-world evidence provides extremely valuable and relevant policy information, but needs to be assessed differently compared with evidence derived from a randomized clinical trial.

pharmacoeconomics

A Practical Guide for Using Registry Data to Inform Decisions About the Cost Effectiveness of New Cancer Drugs: Lessons Learned from the PHAROS Registry