Multiattribute Utility Instruments and Their Use

Richardson, Jeffrey Ralph James; McKie, John; Bariola, Emily

doi:10.1016/b978-0-12-375678-7.00505-8

Cited by 88 publications

(95 citation statements)

References 7 publications

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“…A recent review of generic preference based instruments utilised in published studies between 2005 and 2010 identified the adult version of the EQ-5D as the most prevalent and widely used internationally, having been translated into 150 languages and applied in 63% of the studies identified (Richardson et al, 2014). Other popular generic preference based instruments applied internationally include the Short Form 6 Dimensions (SF-6D) (Brazier et al, 2002) and the Health Utilities Index (HUI) (Torrance et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Ratcliffe

Huynh

Chen

et al. 2016

Social Science & Medicine

115

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In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilized ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an online format in which consenting community based Australian adolescents aged 11 to 17 years (N=1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose 'best' attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of 'worst' attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations.3

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Section: Introductionmentioning

confidence: 99%

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Ratcliffe

Huynh

Chen

et al. 2016

Social Science & Medicine

115

View full text Add to dashboard Cite

show abstract

“…Given the dominant role of the EQ-5D, and the increasing use of the SF-6D [3,4], we consider these two instruments to act as the currencies onto which other instruments' utilities are being converted. In Table 3, for example, the mean utilities (U) within the five 15D utility intervals are computed, together with the corresponding EQ-5D, as well as SF-6D, based utilities.…”

Section: Resultsmentioning

confidence: 99%

“…F-tests and MDDC are shown for each subsample (disease group) to inquire into variations in the degree of non-linearity across disease groups. Considering the extensive use of the EQ-5D and the growing use of the SF-6D [4,5], they have the role as the two alternative target instruments on which each of the other instruments' utilities are being converted. Conversions to the HUI-3 and 15D scales are presented in the Appendix.…”

Section: Discussionmentioning

confidence: 99%

“…A comprehensive review showed that the EQ-5D is the dominant instrument (63.2% of the studies), followed by HUI (2-3) (14.4%), SF-6D (8.8%) and 15D (6.9%) [4]. A recent review confirms the dominant position of the EQ-5D (70%), followed by SF-6D (10%), HUI (5%) and 15D (4%) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Most comparisons of HSU-instruments have included only two instruments [4]. Head-to-head comparisons were identified in 392 studies, suggesting low level of agreement, and hence a need for developing algorithms to compare utility scores across the various instruments used.…”

Section: Introductionmentioning

confidence: 99%

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Health state utility instruments compared: inquiring into nonlinearity across EQ-5D-5L, SF-6D, HUI-3 and 15D

et al. 2015

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2 AbstractPurpose Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated non-linear relationships. This paper inquires into the degree of non-linearity across the four most widely used HSUinstruments, and proposes exchange rates that differ depending on the severity levels of the health state utility scale.Method Overall, 7933 respondents from six countries; 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of non-linear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange Rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument.Result Quantile regressions reveal significant non-linear relationships across all four HSUinstruments. The degrees of non-linearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.19, whilst at the upper end it is 0.35. ConclusionComparisons at different utility levels illustrates the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of non-linear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument 3 been used. Accounting for non-linearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY-gains have been based on different HSU-instruments.

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Valuing health-related quality of life: An EQ-5D-5L value set for England

et al. 2017

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A new version of the EQ-5D, the EQ-5D-5L, is available. The aim of this study is to produce a value set to support use of EQ-5D-5L data in decision-making. The study design followed an international research protocol. Randomly selected members of the English general public completed 10 time trade-off and 7 discrete choice experiment tasks in face-to-face interviews.A 20-parameter hybrid model was used to combine time trade-off and discrete choice experiment data to generate values for the 3,125 EQ-5D-5L health states. Valuation data are available for 996 respondents. Face validity of the data has been demonstrated, with more severe health states generally given lower values. Problems with pain/discomfort and anxiety/depression received the greatest weight. Compared to the existing EQ-5D-3L value set, there are considerably fewer "worse than dead" states (5.1%, compared with over one third), and the minimum value is higher. Values range from −0.285 (extreme problems on all dimensions) to 0.950 (for health states 11211 and 21111). Results have important implications for users of the EQ-5D-5L both in England and internationally. Quality-adjusted life year gains from interventions seeking to improve very poor health may be smaller using this value set and may previously have been overestimated. KEYWORDSEQ-5D-5L, NICE, PROMs, quality of life, stated preferences | INTRODUCTIONHealth care decisions are made under uncertainty, whereby any decision may have a range of different outcomes. To make the "best" decision, potential outcomes need ordering and valuing. Such decisions are made both at the individual level, such as choosing the optimal treatment for a patient, and at the national level, such as choosing how to allocate resources between treatments for different patient groups and across different health conditions. Clinical decisions often affect patients' health-related quality of life (HRQL). Evidence on patients' HRQL can be obtained using patient-reported outcome (PRO) measures. These may be condition specific or generic (see Fayers andMachin, 2016, andLongworth et al., 2014, for further information). Condition-specific PROs focus on specific healthThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Multiattribute Utility Instruments and Their Use

Cited by 88 publications

References 7 publications

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Health state utility instruments compared: inquiring into nonlinearity across EQ-5D-5L, SF-6D, HUI-3 and 15D

Valuing health-related quality of life: An EQ-5D-5L value set for England

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