Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

Lamu, Admassu Nadew; Olsen, Jan Abel

doi:10.1007/s11136-018-1981-6

Cited by 26 publications

(22 citation statements)

References 51 publications

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“…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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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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“…For EORTC QLQ-C30, several mapping algorithms onto EQ-5D-5L index were developed. The recommended direct mapping algorithm in this study has a similar predictive performance to the largest study by Lamu et al [ 18 ], although a direct comparison is not feasible due to the different values sets used. The best model for EQ-5D-5L in Lamu et al was derived from two-part beta regression [ 18 ], which is the same in our study.…”

Section: Discussionmentioning

confidence: 75%

“…The recommended direct mapping algorithm in this study has a similar predictive performance to the largest study by Lamu et al [ 18 ], although a direct comparison is not feasible due to the different values sets used. The best model for EQ-5D-5L in Lamu et al was derived from two-part beta regression [ 18 ], which is the same in our study. Although Lamu et al failed to develop indirect mapping algorithms with a good predictive performance [ 18 ], we developed indirect mapping algorithms with a predictive performance that is comparable to the direct mapping algorithms using ordinal logistic regression.…”

Section: Discussionmentioning

confidence: 75%

“…The best model for EQ-5D-5L in Lamu et al was derived from two-part beta regression [ 18 ], which is the same in our study. Although Lamu et al failed to develop indirect mapping algorithms with a good predictive performance [ 18 ], we developed indirect mapping algorithms with a predictive performance that is comparable to the direct mapping algorithms using ordinal logistic regression. Our indirect mapping algorithm can be used for any value set.…”

Section: Discussionmentioning

confidence: 85%

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Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer

Hagiwara

Shiroiwa

Taira

et al. 2020

Health Qual Life Outcomes

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Background To develop direct and indirect (response) mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy General (FACT-G) onto the EQ-5D-5L index. Methods We conducted the QOL-MAC study where EQ-5D-5L, EORTC QLQ-C30, and FACT-G were cross-sectionally evaluated in patients receiving drug treatment for solid tumors in Japan. We developed direct and indirect mapping algorithms using 7 regression methods. Direct mapping was based on the Japanese value set. We evaluated the predictive performances based on root mean squared error (RMSE), mean absolute error, and correlation between the observed and predicted EQ-5D-5L indexes. Results Based on data from 903 and 908 patients for EORTC QLQ-C30 and FACT-G, respectively, we recommend two-part beta regression for direct mapping and ordinal logistic regression for indirect mapping for both EORTC QLQ-C30 and FACT-G. Cross-validated RMSE were 0.101 in the two methods for EORTC QLQ-C30, whereas they were 0.121 in two-part beta regression and 0.120 in ordinal logistic regression for FACT-G. The mean EQ-5D-5L index and cumulative distribution function simulated from the recommended mapping algorithms generally matched with the observed ones except for very good health (both source measures) and poor health (only FACT-G). Conclusions The developed mapping algorithms can be used to generate the EQ-5D-5L index from EORTC QLQ-C30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms.

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“…In the cost-effectiveness appraisal of competing healthcare programmes across disease areas, there is a growing interest in estimating health outcomes on a generic metric, such as quality-adjusted life years (QALYs) [3]. To obtain the quality adjustment weight in the QALY, generic preference-based measures are used [4]. In many clinical trials, however, condition-or disease-specific non-preference-based measures commonly applied.…”

Section: Introductionmentioning

confidence: 99%

Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets

Lamu

2020

Eur J Health Econ

Self Cite

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Purpose Preference-based measures are essential for producing quality-adjusted life years (QALYs) that are widely used for economic evaluations. In the absence of such measures, mapping algorithms can be applied to estimate utilities from disease-specific measures. This paper aims to develop mapping algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and the English and the US-based EQ-5D-5L value sets. Methods Individuals with heart disease were recruited from six countries: Australia, Canada, Germany, Norway, UK and the US in 2011/12. Both parametric and non-parametric statistical techniques were applied to estimate mapping algorithms that predict utilities for MacNew scores from EQ-5D-5L value sets. The optimal algorithm for each country-specific value set was primarily selected based on root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), and r-squared. Leave-one-out cross-validation was conducted to test the generalizability of each model. Results For both the English and the US value sets, the one-inflated beta regression model consistently performed best in terms of all criteria. Similar results were observed for the cross-validation results. The preferred model explained 59 and 60% for the English and the US value set, respectively. Linear equating provided predicted values that were equivalent to observed values. Conclusions The preferred mapping function enables to predict utilities for MacNew data from the EQ-5D-5L value sets recently developed in England and the US with better accuracy. This allows studies, which have included the MacNew to be used in cost-utility analyses and thus, the comparison of services with interventions across the health system.

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Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

Cited by 26 publications

References 51 publications

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

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 EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer

Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets

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