ObjectiveTo assess the evidence on the validity and responsiveness of five commonly used preference-based instruments, the EQ-5D, SF-6D, HUI3, 15D and AQoL, by undertaking a review of reviews.MethodsFour databases were investigated using a strategy refined through a highly sensitive filter for systematic reviews. References were screened and a search for grey literature was performed. Identified citations were scrutinized against pre-defined eligibility criteria and data were extracted using a customized extraction template. Evidence on known group validity, convergent validity and responsiveness was extracted and reviewed by narrative synthesis. Quality of the included reviews was assessed using a modified version of the AMSTAR checklist.ResultsThirty reviews were included, sixteen of which were of excellent or good quality. The body of evidence, covering more than 180 studies, was heavily skewed towards EQ-5D, with significantly fewer studies investigating HUI3 and SF-6D, and very few the 15D and AQoL. There was also lack of head-to-head comparisons between GPBMs and the tests reported by the reviews were often weak. Where there was evidence, EQ-5D, SF-6D, HUI3, 15D and AQoL seemed generally valid and responsive instruments, although not for all conditions. Evidence was not consistently reported across reviews.ConclusionsAlthough generally valid, EQ-5D, SF-6D and HUI3 suffer from some problems and perform inconsistently in some populations. The lack of head-to-head comparisons and the poor reporting impedes the comparative assessment of the performance of GPBMs. This highlights the need for large comparative studies designed to test instruments’ performance.Electronic supplementary materialThe online version of this article (doi:10.1007/s10198-017-0902-x) contains supplementary material, which is available to authorized users.

PCA and CFA appear useful methods for identifying potential bolt-ons dimensions for an instrument such as the EQ-5D.

This study aimed to develop the Indian 5-level version EQ-5D (EQ-5D-5L) value set, which is a key input in health technology assessment for resource allocation in healthcare. Methods:A cross-sectional survey using the EuroQol Group's Valuation Technology was undertaken in a representative sample of 3548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. The participants were interviewed using a computer-assisted personal interviewing technique. This study adopted a novel extended EuroQol Group's Valuation Technology design that included 18 blocks of 10 composite time trade-off (c-TTO) tasks, comprising 150 unique health states, and 36 blocks of 7 discrete choice experiment (DCE) tasks, comprising 252 DCE pairs. Different models were explored for their predictive performance. Hybrid modeling approach using both c-TTO and DCE data was used to estimate the value set.Results: A total of 2409 interviews were included in the analysis. The hybrid heteroscedastic model with censoring at 21 combining c-TTO and DCE data yielded the most consistent results and was used for the generation of the value set. The predicted values for all 3125 health states ranged from 20.923 to 1. The preference values were most affected by the pain/ discomfort dimension.Conclusions: This is the largest EQ-5D-5L valuation study conducted so far in the world. The Indian EQ-5D-5L value set will promote the effective conduct of health technology assessment studies in India, thereby generating credible evidence for efficient resource use in healthcare.

Background: Generic preference-based measures may miss dimensions important for the health-related quality of life (HRQOL) of patients. When this happens, a possible solution is to add bolt-ons. Finch et al. (Finch AP, Brazier JE, Mukuria C, Bjorner JB. An exploratory study on using principal component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value Health 2017;10:1362e75) have recently shown that bolt-ons can be systematically identified using factor analysis. Nevertheless, because for each bolt-on option a complete re-evaluation may be required, methods to select between them are needed. Objectives: To investigate the possibility of selecting bolt-ons using their ability to predict differences in HRQOL. It tests six factors (energy/vitality, satisfaction, relationships, hearing, vision, and speech), and 37 items loading on them, using the EuroQol five-dimensional questionnaire as a case study. Methods: Data were obtained from the Multi-Instrument Comparison study, an online survey on health and well-being measures carried out in six countries. Two tests were performed. In the first test, linear regressions were fitted to determine whether different bolt-ons helped explain variations in HRQOL as measured by the Health visual analogue scale. The upper anchor (100) of this scale represents excellent physical, mental, and social health, and the lower anchor (0) represents death. Bolt-on relevance was judged comparing the strength, direction, and statistical significance of unadjusted b coefficients. In the second test, linear regressions were fitted to further investigate whether different factors and items helped explain the negative effect of six chronic conditions on HRQOL. A reduction in the coefficients for the chronic condition dummies meant that the factor or item detected the effect. Results: Energy/vitality, relationships, and satisfaction reported substantially larger coefficients than did speech, vision, and hearing. Also, items loading on energy/vitality, relationships, and satisfaction generally presented larger coefficients than did those loading on speech, vision, and hearing. The second test did not detect consistent decrements in the coefficients for chronic conditions when testing factors, but it generally did detect consistent decrements when testing items. Conclusions: The first test appeared useful for bolt-on selection. Further research is needed before using the second test.

The question of whether additional dimensions should be added to the EQ-5D, so-called bolt-ons, has been researched since the 1990s. Several candidate bolt-ons have been tested. The aim of this systematic review was to provide an overview of EQ-5D bolt-on studies, including the origin of possible suitable bolt-ons, their format, and methods that were used to examine their value. Methods: Studies were identified through database search and reference screening and assessed based on a set of inclusion criteria. All studies that investigated bolt-ons for the EQ-5D were eligible for inclusion. Two reviewers independently extracted information from all included studies on objectives, study design, EQ-5D version used, the investigated bolt-ons, methods used to achieve objectives, and outcomes. Results: Of 308 initially identified studies, 28 studies met the inclusion criteria. Of these studies, 3 identified potentially suitable bolt-on dimensions, 13 investigated the psychometric performance of EQ-5D 1 bolt-on(s), and 6 investigated the impact of the bolt-on on health state preferences. In total, 26 bolt-ons were identified, of which cognition was the most frequently mentioned. A wide variety of bolt-on identification methods, psychometric performance tests, and health state valuation methods were used in the included studies. Conclusion: A range of bolt-on dimensions has been investigated using diverse methods. Guidelines are needed to standardize the wording of the bolt-on dimension and response options, evaluate minimal important gain of the bolt-on, and facilitate quality assessment of bolt-on studies. Subsequently, guidelines will facilitate decision making on whether or not to implement a bolt-on dimension to the EQ-5D.