A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

Fernando Alarid-Escudero (), Eline M. Krijkamp, Petros Pechlivanoglou, Hawre Jalal, Szu-Yu Zoe Kao, Alan Yang and Eva A. Enns
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Fernando Alarid-Escudero: Center for Research and Teaching in Economics (CIDE)-CONACyT
Eline M. Krijkamp: Erasmus MC
Petros Pechlivanoglou: The Hospital for Sick Children and University of Toronto
Hawre Jalal: University of Pittsburgh
Szu-Yu Zoe Kao: University of Minnesota School of Public Health
Alan Yang: The Hospital for Sick Children
Eva A. Enns: University of Minnesota School of Public Health

PharmacoEconomics, 2019, vol. 37, issue 11, No 5, 1329-1339

Abstract: Abstract The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Date: 2019
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DOI: 10.1007/s40273-019-00837-x

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