Alarid-Escudero F, Krijkamp E, Pechlivanoglou P, Jalal H, Kao SY, Yang A, Enns EA. A need for change! A coding framework for improving transparency in decision modeling. PharmacoEconomics, 2019;37(11):1329-1339. The coding template is implemented in the R package darthpack.
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Abstract
Background
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.
Methods
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.
Results
We developed the DARTH framework as a way to support transparency, reproducibility, and model sharing in R-based decision analytic models and CEAs. Adoption of this general framework will facilitate the sharing and readability of decision analytic models implemented in R as analysts adopting the framework will be familiar with the component structure and the specific choices and assumptions of each component can be easily scrutinized. The standardization of R code presented here may also support the broader use of R in formal HTA submissions, allowing for more complex modeling methods to be more transparently incorporated into decision making regarding coverage of new health technologies.
Conclusions
The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.