SheffieldR : January 2024 Meetup


January 18, 2024

SheffieldR User Group is back in January 2024 with a Health Economics special and two talks from Robert Smith and Wael Mohammed of Dark Peak Analytics and the University of Sheffield who will talk us through two of their recent publications. Rob will show us how to automate Health Economic Evaluation with R without sharing sensitive data and Wael will guide us through a tutorial on R packaging for cost-effectiveness models.

Meeting is 2024-01-18 17:00-18:30 in The Diamond G04 Workroom 1. Sign up here. Remote attendance is possible using Google Meet.

If you have any questions or enquiries please get in touch with Grace Accad or Neil Shephard.

Robert Smith

Living HTA: Automating Health Economic Evaluation with R

Schematic showing the interaction between the company API (application programming interface) and the consultant automated workflow.

Background: Requiring access to sensitive data can be a significant obstacle for the development of health models in the Health Economics & Outcomes Research (HEOR) setting. We demonstrate how health economic evaluation can be conducted with minimal transfer of data between parties, while automating reporting as new information becomes available.

Methods: We developed an automated analysis and reporting pipeline for health economic modelling and made the source code openly available on a GitHub repository. The pipeline consists of three parts: An economic model is constructed by the consultant using pseudo data. On the data-owner side, an application programming interface (API) is hosted on a server. This API hosts all sensitive data, so that data does not have to be provided to the consultant. An automated workflow is created, which calls the API, retrieves results, and generates a report.

Results : The application of modern data science tools and practices allows analyses of data without the need for direct access – negating the need to send sensitive data. In addition, the entire workflow can be largely automated: the analysis can be scheduled to run at defined time points (e.g. monthly), or when triggered by an event (e.g. an update to the underlying data or model code); results can be generated automatically and then be exported into a report. Documents no longer need to be revised manually.

Conclusions: This example demonstrates that it is possible, within a HEOR setting, to separate the health economic model from the data, and automate the main steps of the analysis pipeline.

Biography - Robert Smith

Rob is a health economist based in Sheffield, UK. His research focuses on the methods used to estimate the costs and benefits of public health interventions, with a specific interest in microsimulation modelling in R. He is a expert advisor in Public Health Economics & Decision Science to the WHO-HEAT project. He is currently working at the Joint Biosecurity Centre to help inform the UK government response to the pandemic.

Wael Mohammed

Packaging cost-effectiveness models in R: A Tutorial

Schematic of a typical model structure taking raw data and user inputs and using a set of functions to return results in the form of data, publication tables, and figures.

Background: The use of programming languages such as R in health economics and decision science is increasing, and brings numerous benefits including increasing model development efficiency, improving transparency, and reducing human error. However, there is limited guidance on how to best develop models using R. So far, no clear consensus has emerged.

Methods: We present the advantages of creating health economic models as R packages - structured collections of functions, data sets, tests, and documentation. Assuming an intermediate understanding of R, we provide a tutorial to demonstrate how to construct a basic R package for health economic evaluation. All source code used in or referenced by this paper is available under an open-source licence.

Case Study: We use the Sick Sicker Model as a case study applying the steps from the tutorial to standardise model development, documentation and aid review. This can improve the distribution of code, thereby streamlining model development, and improving methods in health economic evaluation.

Conclusion: R packages offer a valuable framework for enhancing the quality and transparency of health economic evaluation models. Embracing better, more standardised software development practices, while fostering a collaborative culture, has the potential to significantly improve the quality of health economic models, and, ultimately, support better decision making in healthcare.

Biography - Wael Mohammed

Wael is a health economist with a background in pharmacy and public health. In addition, he has experience in decision-analytic modelling, econometrics, and data science. He is currently pursuing a PhD in public health, economics and decision science at the University of Sheffield.