🌟 Major Milestone in Health Economics Research 🌟
I am happy to share that our latest work, titled "Systematic review reveals that EQ-5D minimally important differences decrease with increasing baseline score and may vary with treatment type", has officially been published in the Journal of Clinical Epidemiology and is now available as open access, thanks to the support from the EuroQol Research Foundation. You can access it here: https://lnkd.in/gr4bXk67.
This publication serves as a pivotal step forward in health economics research, addressing frequent questions we receive from academics, healthcare professionals, and industry partners, such as:
"What is the MID for EQ-5D index and EQ VAS score?"
"How can it be used to determine the clinical importance of a treatment effect?"
In this paper, our team conducted a comprehensive evidence synthesis on anchor-based minimal important differences (MIDs) for the EQ-5D instruments, a widely-used, generic measure of health-related quality of life, with two adult versions: EQ-5D-3L and EQ-5D-5L.
We pooled 210 MID estimates from 47 studies, broken down into:
- 122 estimates for EQ-5D-3L index scores
- 51 estimates for EQ-5D-5L index scores
- 47 estimates for EQ VAS
💡 Key Implications and Practical Applications: Our review introduces a user-friendly method using formulas and an established reference table for EQ-5D users to determine MIDs tailored to their specific contexts. This approach simplifies the process compared to the complex protocols often required in the literature.
NOTE: Our latest revision made some minor updates where the nonlinear polynomial model outperformed other models in predicting the MIDs of improved scores. The formulas have also been updated accordingly (Refer to the third image), or you can derive the MID scores from Table 4 in the paper.
For health deterioration, we recommend using the overall average MIDs (mean: -0.02 for EQ-5D-3L, -0.04 for EQ-5D-5L, and -6.5 for EQ VAS) to interpret changes in health status.
📊 This work advances the understanding and use of EQ-5D data, making it more accessible and actionable for healthcare decision-making. It provides a valuable benchmark for interpreting changes in health outcomes and enhances the precision of health evaluations.
Lastly, a special thank you to my incredible mentors Nan Luo, Michael Herdman, and my colleagues Le Ann Chen and Jing Ying Cheng for their invaluable support. 🔬🤝
#HealthEconomics #Research #EQ5D #MID #QualityOfLife #Healthcare