Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis: An ISPOR Special Interest Group Report.

Abstract

Objectives: Health economic models are crucial for health technology assessments to evaluate the value of medical interventions. Open-source models (OSMs), in which 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 8664 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.

Conclusions: This review highlights the diversity and availability of open-source models (OSMs) in health economics, predominantly hosted on GitHub and developed using R. The models span various medical fields, with a strong focus on infectious diseases, oncology, and neurology. Ensuring clear licensing and standardized reporting is crucial to maximizing their impact. A combined approach of repository searches and traditional literature reviews provides a comprehensive method for identifying OSMs. Future efforts should enhance search strategies, improve reporting standards, and leverage OSMs to inform health policy decisions.