Introduction

Health economic evaluations play a crucial role in the comprehensive assessment of health technologies as part of Health Technology Assessment (HTA) processes. These evaluations provide valuable insights into the economic impact and cost-effectiveness of healthcare interventions, enabling policymakers, healthcare providers, and payers to make informed decisions. Through the use of various economic evaluation techniques such as cost-effectiveness analysis, cost-utility analysis, and cost-benefit analysis, health economic evaluations assess the relative value of different interventions by comparing their costs to their health outcomes. By considering both the costs and benefits of healthcare technologies, these evaluations help optimize resource allocation, identify cost-saving opportunities, and prioritize interventions that provide the greatest value for money. Additionally, health economic evaluations contribute to transparency, equity, and sustainability in healthcare decision-making, promoting the efficient allocation of limited healthcare resources and ultimately improving overall population health.

Furthermore, by making health economic evaluations living, decision-makers can not only assess the current value of healthcare interventions but also ensure the ongoing relevance and effectiveness of resource allocation strategies. The value of making health economic evaluations “living” lies in their ability to be continuously updated and re-evaluated over time. By incorporating a dynamic and iterative approach, these evaluations can adapt to new evidence, changes in healthcare practices, and evolving technologies. This allows decision-makers to monitor the long-term cost-effectiveness and value of healthcare interventions, ensuring that the allocation of resources remains optimal and aligned with current knowledge and priorities. By regularly reviewing and updating the analysis, the benefits, risks, and costs associated with different health technologies can be reassessed, providing decision-makers with timely information to inform resource allocation decisions and policy adjustments. Moreover, a living approach to health economic evaluations promotes transparency and accountability, as stakeholders can access and track the latest information and findings, facilitating evidence-based decision-making and fostering trust in the healthcare system.

Programming languages are vital for health economic evaluations, enabling efficient analysis of large datasets and implementation of complex models. While Excel has been a primary tool for basic analysis, it has limitations in handling large datasets and advanced statistical analyses. These limitations highlight the need for more specialized programming languages and software packages that offer greater computational power, robust statistical capabilities, and enhanced error-checking mechanisms to ensure the accuracy and reliability of health economic analyses. R, a widely adopted programming language, offers a vast array of specialized packages and libraries, supporting advanced statistical techniques, modeling approaches, and enhance the way of communicating health economic results to different stakeholders. R has garnered strong recognition and endorsement from health technology assessment (HTA) bodies worldwide, making it the recommended programming language for health economic evaluations. Its utilization by renowned organizations, such as the National Institute for Health and Care Excellence (NICE) in the UK, the Canadian Agency for Drugs and Technologies in Health (CADTH), and The National Health Care Institute (Zorginstituut) in the Netherlands underscores its credibility and suitability for conducting rigorous HTA analyses. This widespread endorsement further solidifies R’s position as a trusted and preferred tool for health economic evaluations at a global scale.

However, the transition from Excel to R in health economics and HTA faces barriers such as the learning curve associated with programming concepts and syntax. Excel’s user-friendly interface may be more accessible to users without extensive programming knowledge. Compatibility concerns between Excel-based models and R, as well as organizational barriers and limited resources, can hinder adoption. Perceived risks associated with open-source software may also influence decision-making. Overcoming these barriers requires investment in training, addressing compatibility concerns, promoting a cultural shift, and building trust in the benefits of R for health economic evaluations and HTA.

This work aims to present a user-friendly framework for newcomers to R with minimal coding experience, guiding them through the development of a living health economic evaluation. The framework facilitates a clear understanding of the entire process and allows users to make progress through incremental steps in creating a dynamic health economic evaluation using R. Moreover, the applicability of the framework will be demonstrated in a real-world example.

A step-wise framework

The first draft of a step-wise framework for living HEE is demonstrated in the figure below:

A stepwise framework of living health economic evaluation

A stepwise framework of living health economic evaluation

Develop a health economic model

Create lists of input parameter

Input parameters should be divided into 2 groups:

  1. Fixed parameters:

  • User definable

  • Others

  1. Varying parameters

Develop functions for all analysis

  1. A function for base-case analysis

  2. A function to generate a psa dataset

  3. A function to run psa analysis

By turning the base-case function into the psa function, the analyst can make the analyses transparent and consistent.

  1. A wrapper function of user definable inputs to return result of PSA analysis

Write a report or manuscript

Present the result in different ways

  1. Technical report

  2. Live report/ manuscript

  3. Shiny app

Make the HEE living

Apply the framework in a realworld example

Cost-effectiveness of Herpes Zoster vaccination program in overall 70 years old cohort in the Netherlands. This is a simple Markov model which is a part of my master thesis. It was built in Excel but transformed well to R using the proposed framework. If you have a better example, I’m happy to replace.

Discussion

….to be determined later…