Protocol for a systematic review and meta-analysis of minimal important differences for generic multiattribute utility instruments

Abstract

Introduction Generic multiattribute utility instruments (MAUIs) are efficient tools for determining and enumerating health-related quality of life. MAUIs accomplish this by generating health state utilities (HSUs) via algorithms. Minimal important differences (MIDs) assist with the interpretation of HSUs by estimating minimum changes that are clinically significant. The overall goal of the proposed systematic review and meta-analysis is the development of comprehensive guidelines for MID estimation. Methods and analysis This protocol defines a systematic review and meta-analysis of MIDs for generic MAUIs. The proposed research will involve a comprehensive investigation of 10 databases (EconLit, IDEAs database, INAHTA database, Medline, PsycINFO, Embase, Emcare, JBIEBP and CINAHL) from 1 June 2022 to 7 June 2022, and will be performed and reported in accordance with several validated guidelines, principally the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The quality of papers, considered for inclusion in the review, will be appraised using the COnsensus-based Standards for the selection of health Measurement INstruments, inter alia. Narrative analysis will involve identifying the characteristics of MIDs including methods of calculation, sources of heterogeneity, and validation. Meta-analysis will also be conducted. The descriptive element of meta-analysis will involve the generation of I 2 statistics and Galbraith plots of MID heterogeneity. Together with narrative analysis, this will allow sources of MID heterogeniety to be identified. A multilevel mixed model, estimated via restricted maximum likelihood estimation, will be constructed for the purposes of meta-regression. Meta-regression will attempt to enumerate the effects of sources of heterogeneity on MID estimates. Meta-analysis will be concluded with pooling of MIDs via a linear random-effects model. Ethics and dissemination Ethics approval is not required for this review, as it will aggregate data from published literature. Methods of dissemination will include publication in a peer-reviewed journal, as well as presentation at conferences and seminars. PROSPERO registration number CRD42021261821.