Development of Methods for the Mapping of Utilities Using Mixture Models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in Patients with Asthma

Gray, Laura A.; Alava, Mónica Hernández; Wailoo, Allan

doi:10.1016/j.jval.2017.09.017

Cited by 40 publications

(56 citation statements)

References 28 publications

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“…The ndings provide evidence that different predictive models should be used for mapping EQ-5D-3L, EQ-5D-5L, HUI3 and SF-6D in our sample. Our analyses showed that both versions of the EQ-5D utility scores were best predicted by the beta mixture regression model which is consistently reported in other studies [14][15][16]. Our mapping algorithm for the HUI3…”

Section: Discussionsupporting

confidence: 85%

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Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in Patients with Depression

Abdin¹,

Chong²,

Seow³

et al. 2020

Preprint

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There is limited evidence of mapping the clinical instrument to a generic preference-based instrument in an Asian patient population. The current study aims to map the eight-item Patient Health Questionnaire depression scale (PHQ) onto the EuroQol five-dimensional (EQ-5D), the Health Utilities Index Mark 3 (HUI3) and the Short Form-6D SF-36 to inform future in cost-utility analyses for treatment in depression sample. A total of 249 participants who have completed PHQ-8, EQ5D, SF6D and HUI-3 questionnaires were included in the analyses. A beta regression mixture model was used to map the utility scores. The results were compared against two common regression methods including Ordinary Least Square (OLS) and Tobit regression models. The mean age of the sample was 36.2 years (SD=11.1). The mean EQ-5D-3L, EQ-5D-5L, HUI3 and SF-6D utility scores were 0.615, 0.709, 0.461 and 0.607, respectively. The EQ-5D-3L, EQ-5D-5L and SF-6D utility scores were best predicted by the beta mixture regression model consisting of PHQ total sores, PHQ-squared, and covariates including age and gender. The HUI3 was best predicted by the OLS model. The current study provides important evidence to clinicians and researchers about the mapping algorithms that can be used in economics evaluation among patients with depression.

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Section: Discussionsupporting

confidence: 85%

“…The beta regression mixture model is a two-part model that incorporates a multinomial logit model and a beta mixture model in their algorithms. Studies have increasingly suggested that this regression method outperforms linear regression model [14][15][16].…”

Section: Statistical Analysesmentioning

confidence: 99%

Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in Patients with Depression

Abdin¹,

Chong²,

Seow³

et al. 2020

Preprint

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“…These regression methods can be extended to mapping to EQ-5D HSUVs from other country value sets as well as other measures e.g. Gray et al 2018 [212] uses these approaches for HUI3. The appropriateness of methods will depend on the measure and distribution as well as standard tests applied to assess these methods.…”

Section: Discussionmentioning

confidence: 99%

“…Some populations were mixed, for example, Chen, 2015 [62], Richardson, 2014 [63] used the Multi-instrument Comparison (MIC) dataset [253] which has all the generic PBMs and contains both patients (selfidentified) and members of the general population who were healthy. A number of studies relied on the MIC dataset to undertake mapping within specified patient populations including asthma [69,212], depression [64], diabetes [65,202], cancer [205] and heart disease [68].…”

Section: General Description Of Studiesmentioning

confidence: 99%

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

Mukuria

Rowen

Harnan

et al. 2019

Appl Health Econ Health Policy

103

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Short Running Title: Updated systematic review of studies mapping to preference-based measures Key points for decision-makers  Mapping or cross-walking enables utility values to be generated from other health related quality of life measures in studies where the preferred preference-based measure has not been used  This updated review shows that compared to 10 years ago, there are now many mapping functions (n=233 across 180 published studies) which can be used to generate utility values from a large number of measures with the majority being to EQ-5D-3L  Mapping studies now report important information that enable decision-makers to better understand and interpret mapped utility estimates, but mapping remains a second-best alternative to using the preference-based measures directly

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“…The probability of being in each component is estimated using a multinomial logit model. In this study, we used a beta-mixture model which has recently been introduced by Gray et al [15,16] based on the truncated inflated beta regression model [17]. This is a two-part model including a multinomial logit model and a beta-mixture model.…”

Section: Discussionmentioning

confidence: 99%

Mapping EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)

et al. 2019

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Purpose To develop a mapping model to estimate EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Methods The responses to EQ-5D-3L and KOOS questionnaires (n = 40,459 observations) were obtained from the Swedish National anterior cruciate ligament (ACL) Register for patients ≥ 18 years with the knee ACL injury. We used linear regression (LR) and beta-mixture (BM) for direct mapping and the generalized ordered probit model for response mapping (RM). We compared the distribution of the original data to the distributions of the data generated using the estimated models. Results Models with individual KOOS subscales performed better than those with the average of KOOS subscale scores (KOOS 5 , KOOS 4). LR had the poorest performance overall and across the range of disease severity particularly at the extremes of the distribution of severity. Compared with the RM, the BM performed better across the entire range of disease severity except the most severe range (KOOS 5 < 25). Moving from the most to the least disease severity was associated with 0.785 gain in the observed EQ-5D-3L. The corresponding value was 0.743, 0.772 and 0.782 for LR, BM and RM, respectively. LR generated simulated EQ-5D-3L values outside the feasible range. The distribution of simulated data generated from the BM model was almost identical to the original data. Conclusions We developed mapping models to estimate EQ-5D-3L from KOOS facilitating application of KOOS in costutility analyses. The BM showed superior performance for estimating EQ-5D-3L from KOOS. Further validation of the estimated models in different independent samples is warranted.

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Development of Methods for the Mapping of Utilities Using Mixture Models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in Patients with Asthma

Cited by 40 publications

References 28 publications

Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in Patients with Depression

Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in Patients with Depression

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

Mapping EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)

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