What Determines the Shape of an EQ-5D Index Distribution?

Parkin, David; Devlin, Nancy; Feng, Yan

doi:10.1177/0272989x16645581

Cited by 32 publications

(13 citation statements)

References 38 publications

(39 reference statements)

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“…We examined the distribution of EQ-5D index scores and EQ-VAS scores which revealed a skewed distribution for the EQ-5D scores, similar to what has been reported elsewhere [ 16 ]. EQ-VAS scores approximated a normal distribution.…”

Section: Methodsmentioning

confidence: 70%

Factors influencing health-related quality of life in patients with Type 1 diabetes

Raymakers

Gillespie

O’Hara

et al. 2018

Health Qual Life Outcomes

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AimsGeneric, preference-based measures of health-related quality of life (HRQoL) are a common input to the economic evaluation of new health technologies. As such, it is important to explore what characteristics of patients with Type 1 diabetes might impact scores on such measures.MethodsThis study utilizes baseline data from a cluster-randomized trial that recruited patients with Type 1 diabetes at six centers across Ireland. Health-related quality of life was assessed using the three-level EuroQol EQ-5D (EQ-5D) measure. Patients’ responses to individual dimensions of the EQ-5D were explored. To see which patient factors influenced EQ-5D scores, multivariate regression analysis was conducted with EQ-5D scores as the outcome variable.ResultsData was available for 437 Type 1 diabetes patients. The median age of these patients was 40 (IQR: 31-49) years and 53.8% were female. Overall, patients reported a high HRQoL based on EQ-5D scores (0.87 (SD: 0.19). Fifty-four percent of patients reported a perfect HRQoL. For those that reported problems, the most common dimension was the anxiety/depression dimension of the EQ-5D (29.6%). In the multivariate regression analysis, self-reported mental illness (− 0.22 (95% CI: -0.34, − 0.10)) and being unemployed (− 0.07 (95% CI: -0.13, − 0.02)) were negatively associated with EQ-5D scores (p < 0.05). The influence of self-reported mental illness was persistent in sensitivity analyses.ConclusionsThe study results indicate that patients with Type 1 diabetes report a high HRQoL based on responses to the EQ-5D. However, there are a substantial number of Type 1 diabetes patients that report problems in the anxiety/depression dimension, which may provide avenues to improve patients’ HRQoL.Trial registrationCurrent Controlled Trials ISRCTN79759174.Electronic supplementary materialThe online version of this article (10.1186/s12955-018-0848-4) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 70%

Factors influencing health-related quality of life in patients with Type 1 diabetes

Raymakers

Gillespie

O’Hara

et al. 2018

Health Qual Life Outcomes

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“…Country-specific 3L and 5L utility values were calculated for each value set for all condition groups combined and described numerically and graphically using histograms. We examined clusters and discontinuities (‘gaps’) in the histograms as such patterns theoretically diminish the sensitivity and the accuracy of the instruments and might lead to estimation problems [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Is EQ-5D-5L Better Than EQ-5D-3L? A Head-to-Head Comparison of Descriptive Systems and Value Sets from Seven Countries

2018

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ObjectiveThis study describes the first empirical head-to-head comparison of EQ-5D-3L (3L) and EQ-5D-5L (5L) value sets for multiple countries.MethodsA large multinational dataset, including 3L and 5L data for eight patient groups and a student cohort, was used to compare 3L versus 5L value sets for Canada, China, England/UK (5L/3L, respectively), Japan, The Netherlands, South Korea and Spain. We used distributional analyses and two methods exploring discriminatory power: relative efficiency as assessed by the F statistic, and an area under the curve for the receiver-operating characteristics approach. Differences in outcomes were explored by separating descriptive system effects from valuation effects, and by exploring distributional location effects.ResultsIn terms of distributional evenness, efficiency of scale use and the face validity of the resulting distributions, 5L was superior, leading to an increase in sensitivity and precision in health status measurement. When compared with 5L, 3L systematically overestimated health problems and consequently underestimated utilities. This led to bias, i.e. over- or underestimations of discriminatory power.ConclusionWe conclude that 5L provides more precise measurement at individual and group levels, both in terms of descriptive system data and utilities. The increased sensitivity and precision of 5L is likely to be generalisable to longitudinal studies, such as in intervention designs. Hence, we recommend the use of the 5L across applications, including economic evaluation, clinical and public health studies. The evaluative framework proved to be useful in assessing preference-based instruments and might be useful for future work in the development of descriptive systems or health classifications.

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“…A further obstacle to rescuing the QALY is the fact that the utility manifest score can take negative values. This has been shown across many disease states for both the EQ-5D-3L and EQ-5D-5L 10 , 11 . In the former, the lowest possible manifest score, as noted above, is −0.59; in the latter the lowest score is −0.29.…”

Section: Abandoning the Qalymentioning

confidence: 67%

Measurement, modeling and QALYs

Langley

McKenna

2020

F1000Res

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Over the past 30 years, a mainstay of health technology assessment has been the creation of modeled incremental cost-per-quality adjusted life year (QALY) claims. These are intended to inform resource allocation decisions. Unfortunately, the reliance on the construction of QALYs from generic utility scales is misplaced. Those advocating QALY-based lifetime modeled claims fail to appreciate the limitations placed on these constructs by the axioms of fundamental measurement. Utility scales, such as those created by the EQ-5D-3L instrument, are nothing more than multidimensional, ordinal scales. Such scales cannot support basic arithmetic operations. Interval scales can support addition and subtraction; ratio scales the further operations of multiplication and division. Those who advocate the construction of QALYs fail to appreciate that such an operation is only possible if the utility scale is unidimensional and has ratio properties with a true zero. The utility measures available do not meet these requirements. As we cannot produce meaningful utility values, the QALY is an invalid construct. Consequently, cost-per-incremental QALY claims are impossible to sustain and the application of cost-per QALY thresholds meaningless. As utility is a latent, unidimensional variable, the best a measure of utility could achieve would be unidimensionality and interval scaling properties. Where such measures are available, they could support claims for response to therapy. Consequently, there would be no need to continue constructing imaginary lifetime value assessment frameworks. Admitting that the QALY is a fatally flawed construct means rejecting 30 years of cost-per-QALY models.

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What Determines the Shape of an EQ-5D Index Distribution?

Cited by 32 publications

References 38 publications

Factors influencing health-related quality of life in patients with Type 1 diabetes

Factors influencing health-related quality of life in patients with Type 1 diabetes

Is EQ-5D-5L Better Than EQ-5D-3L? A Head-to-Head Comparison of Descriptive Systems and Value Sets from Seven Countries

Measurement, modeling and QALYs

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