A systematic review of minimum important changes for generic multi-attribute utility instruments and recommendations for their estimation.
作者信息
Henson Glen J, van der Mei Ingrid, Taylor Bruce V, Scuffham Paul, Chen Gang, Campbell Julie A
机构信息
Menzies Institute for Medical Research (University of Tasmania), 17 Liverpool St, Hobart, TAS, 7000, Australia.
Menzies Health Institute Queensland (Griffith University), G40 Griffith Health Centre, Level 8.86 Gold Coast Campus Griffith University, Southport, QLD, 4215, Australia.
出版信息
Eur J Health Econ. 2025 Apr 16. doi: 10.1007/s10198-025-01778-3.
INTRODUCTION
Minimum important changes (MICs) represent thresholds for clinically meaningful change. Multi-attribute utility instruments (MAUIs) generate health state utilities (holistic measures of health-related quality of life). No systematic review of MICs specifically for MAUIs has been conducted. In addition, no guidelines for estimating MICs for MAUIs have been proposed. We aimed to correct these evidence gaps by producing guidelines contextualised by a systematic review.
METHODS
We searched ten databases for relevant records using various search terms. Extracted data were analysed narratively and descriptively. The presence of key reporting items (relating to precision, sensitivity, and concurrent validity) was also evaluated. Guidelines for MIC estimation were informed by the broader MIC literature and contextualised using study results.
RESULTS
The review identified 5035 non-duplicate records, with 68 entering the study. 282 unique, anchor-based MICs were extracted. Of these MICs, 119 (42.20%) pertained to the EQ-5D-3L, 82 (29.08%) to the EQ-5D-5L, and 50 (17.73%) to the SF-6D.v1. The most common anchor-based method used to estimate MICs (107, 37.94%) involved taking the mean change score for a group considered to have experienced a MIC. Distribution-based methods were also common, appearing in 31 (45.59%) of the included studies. The inclusion of key reporting items was generally deficient.
CONCLUSIONS
Deficiencies in reporting and diverse estimation methods raise concerns regarding the extant MAUI MIC literature. Researchers should exercise caution when using existing MAUI MICs. Recommendations presented in our study may assist researchers in effectively estimating MICs for use in health economics.