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

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.

Making health economic models Shiny: A tutorial

“…While the focus of this tutorial is on the application of Shiny for health economic models, below we provide a brief overview of the "Sick-Sicker model". For further details, readers are encouraged to consult previous publications by the DARTH group 15 , 16 , 18 and the DARTH group website 17 .…”

“…We therefore begin by describing the two functions f_gen_psa and f_MM_sicksicker in more detail before moving on to demonstrate how to create a user-interface. In this tutorial, we follow Alarid-Escudero et al’s (2019) coding framework and add to it the prefix 'f_' to denominate functions 15 .…”

“…Normally we would use a custom function that creates a publication quality table that is aesthetically pleasing. There are different packages that provide this functionality 15 , 20 , 21 .…”

“…Here, we provide a simple example of a Shiny web app, using a general four-state Markov model. The model is based on the ’Sick-Sicker model’, which has been described in detail in previous publications 15 , 16 and in open source teaching materials by the DARTH workgroup 17 . The model was slightly adapted to implement probabilistic sensitivity analysis.…”

Improving Transparency in Decision Models: Current Issues and Potential Solutions

“…Three of the papers in this special issue focus on more applied aspects of model transparency, particularly with respect to the software or approach used to implement decision-analytic models. Alarid-Escudero et al [7] set out the Decision Analysis in R for Technologies in Health (DARTH) coding framework-a high-level approach for implementing health economic models in R, including recommendations for conceptualizing, modularizing, and coding the model, with the intention of allowing these to be more easily scrutinized and understood by other model users. In a more applied setting, Jansen et al [8] [9] discusses the importance of transparency in model implementation and how Discretely Integrated Condition Event (DICE) simulation might help in achieving this objective.…”