Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis.

Abstract

Introduction: Health economic models are crucial for health technology assessment to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviewed databases to map the landscape of available OSMs in health economics.

Methods: A systematic database review was conducted, informed by guidance from ISPOR's OSM Special Interest Group. Eleven databases and specific OSM repositories were searched using predefined terms. Identified models were screened and duplicates were removed.

Results: The search yielded 8,664 hits, resulting in 182 unique OSMs. GitHub hosted the majority (74%), followed by Zenodo (11%). R was the predominant software platform (64%). Infectious disease was the most common application domain (29%). Markov models were the most frequent model type (49%). Licensing with Creative Commons was typical. Government and academic institutions were the primary sponsors, although many models lacked clear sponsorship.

Discussion: This review identified a diverse array of OSMs primarily hosted on GitHub and developed using R. The models covered a wide range of medical fields, with a majority focus on infectious diseases. Licensing clarity and standardized reporting are essential to maximize OSM impact. Combining repository searches with a traditional literature search provides a comprehensive approach to identifying OSMs.

Conclusion: This review highlighted the availability and diversity of OSMs in health economics, predominantly utilizing R and focusing on infectious disease, oncology, and neurology. Future work should enhance search capabilities, standardize model reporting, and leverage OSMs for health policy impacts.