Achieving Appropriate Model Transparency: Challenges and Potential Solutions for Making Value-Based Decisions in the United States. - Carlson, Josh J.; Walton, Surrey M.; Basu, Anirban; Chapman, Richard H.; Campbell, Jonathan D.; McQueen, R. Brett; Pearson, Steven D.; Touchette, Daniel R.; Veenstra, David; Whittington, Melanie D.; Ollendorf, Daniel A. (PharmacoEconomics, 2019)
Transparency in decision modeling remains a topic of rigorous debate among healthcare stakeholders, given tensions between the potential benefits of external access during model development and the need to protect intellectual property and reward research investments. Strategies to increase decision model transparency by allowing direct external access to a model's structure, source code, and data can take on many forms but are bounded between the status quo and free publicly available open-source models. Importantly, some level of transparency already exists in terms of methods and other technical specifications for published models. The purpose of this paper is to delineate pertinent issues surrounding efforts to increase transparency via direct access to models and to offer key considerations for the field of health economics and outcomes research moving forward from a US academic perspective. Given the current environment faced by modelers in academic settings, expected benefits and challenges of allowing direct model access are discussed. The paper also includes suggestions for pathways toward increased transparency as well as an illustrative real-world example used in work with the Institute for Clinical and Economic Review to support assessments of the value of new health interventions. Potential options to increase transparency via direct model access during model development include adequate funding to support the additional effort required and mechanisms to maintain security of the underlying intellectual property. Ultimately, the appropriate level of transparency requires balancing the interests of several groups but, if done right, has the potential to improve models and better integrate them into healthcare priority setting and decision making in the US context.