Plain Language Summary

This research explores how the EQ-5D, a tool measuring health-related quality of life, is used and analyzed in clinical trials. The EQ-5D is widely used in randomized clinical trials, but it is unclear what and how statistical methods have been used.

The review analyzed data from 2125 randomized clinical trials that used EQ-5D, focusing on methods used to evaluate treatment effects. EQ-5D data can be categorized into 3 types: (1) dimension responses, (2) utility values, and (3) a visual analog scale. Utility values and the visual analog scale, which provide a single numerical score for health status, were the most frequently analyzed. Dimension responses, which offer detailed insights into specific health aspects like mobility and anxiety/depression, were less commonly used.

The study revealed substantial variation in the statistical models chosen for analysis. Linear fixed-effect models were commonly used for single post-baseline data, while linear mixed-effect models were preferred for multiple post-baseline analyses. Despite recommendations, many trials did not adjust for baseline differences or handle missing data appropriately, potentially compromising the accuracy of their findings.

For patients and healthcare decision makers, this research highlights the importance of consistent and reliable analysis methods in trials using EQ-5D. Understanding how different treatments impact health-related quality of life can guide better healthcare choices and policy decisions.

For researchers, the study underscores the need for standardized statistical methods when analyzing EQ-5D data. It suggests a need for guidelines to ensure baseline adjustments and robust handling of missing data. Such guidelines could improve the comparability of trial results and support more accurate clinical and economic evaluations.

Overall, while EQ-5D is a valuable tool for capturing patient-reported outcomes, the variation in analysis methods suggests there may be potential room for improvement in its analysis in clinical trials. By developing clear guidelines and improving statistical practices, the EQ-5D can more effectively inform healthcare decisions and policy development.

^{Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.}