Budget Impact Models for Lung Cancer Interventions: A Systematic Literature Review
Willis M, Nilsson A, Kellerborg K, Thet Lwin ZM, Prelaj A, on behalf of the I3LUNG Project Investigators
Background
Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence (AI)-based clinical-decision tool to identify non-small cell lung cancer patients most likely to benefit from immunotherapy. We aim to evaluate the budgetary implications and this study describes a systematic literature review of published Lung Cancer (LC) BIMs.
Methods
We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modelling methods employed, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines.
Results
A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, four compared non-drug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time.
Conclusion
The number of published BIMs for lung cancer exceeded expectations. There were modest trends towards publication frequency and model quality over time. Our analysis revealed variability across the models as well as their adherence to best practices, indicating substantial room for improvement. While none of the models were individually suitable for the purpose of evaluating an artificial intelligence–based treatment selection tool, some models provided valuable insights.
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Journal of Managed Care & Specialty Pharmacy, 2024;30(9)
DOI: https://doi.org/10.18553/jmcp.2024.30.9.104
