Abstract

Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of healthcare resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). Although these tools may be sufficient for relatively simple analyses, they put unnecessary constraints on the analysis that may ultimately limit its credibility and relevance. In contrast, modern programming languages such as R, Python, Matlab, and Julia facilitate the development of models that are (i) clinically realistic, (ii) capable of quantifying decision uncertainty, (iii) transparent and reproducible, and (iv) reusable and adaptable. An HTA environment that encourages use of modern software can therefore help ensure that coverage and pricing decisions confer greatest possible benefit and capture all scientific uncertainty, thus enabling correct prioritization of future research.