Skip to main content
Top
Published in: The European Journal of Health Economics 2/2021

01-03-2021 | Original Paper

Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique

Authors: Lan Gao, Wei Luo, Utsana Tonmukayakul, Marj Moodie, Gang Chen

Published in: The European Journal of Health Economics | Issue 2/2021

Login to get access

Abstract

Background

This study aims to derive country-specific EQ-5D-5L health status utility (HSU) from the MacNew Heart Disease Health-related Quality of Life questionnaire (MacNew) using both traditional regression analyses, as well as a machine learning technique.

Methods

Data were drawn from the Multi-Instrument Comparison (MIC) survey. The EQ-5D-5L was scored using 4 country-specific tariffs (United States, United Kingdom, Germany, and Canada). The traditional regression techniques, as well as a machine learning technique, deep neural network (DNN), were adopted to directly predict country-specific EQ-5D-5L HSUs (i.e. a direct mapping approach). An indirect response mapping was undertaken additionally. The optimal algorithm was identified based on three goodness-of-fit tests, namely, the mean absolute error (MAE), mean error (ME) and root mean square error (RMSE), with the first being the primary criteria. Internal validation was undertaken.

Results

Indirect response mapping and direct mapping (via betamix with MacNew items as the key predictors) were found to produce the optimal mapping algorithms with the lowest MAE when EQ-5D-5L were scored using three country-specific tariffs (United Kingdom, Canada, and Germany for the former and United Kingdom, United States, Canada and Germany for the latter approach). DNN approach generated the lowest MAE and RMSE when using the Germany-specific tariff.

Conclusions

Among different approaches been explored, there is not a conclusive conclusion regarding the optimal method for developing mapping algorithms. A machine learning approach represents an alternative mapping approach that should be explored further. The reported algorithms from response mapping have the potential to be more widely used; however, the performance needs to be externally validated.
Appendix
Available only for authorised users
Literature
1.
go back to reference Rumsfeld, J.S., Alexander, K.P., Goff, D.C., Jr., Graham, M.M., Ho, P.M., Masoudi, F.A., Moser, D.K., Roger, V.L., Slaughter, M.S., Smolderen, K.G., Spertus, J.A., Sullivan, M.D., Treat-Jacobson, D., Zerwic, J.J., American Heart Association Council on Quality of, C., Outcomes Research, C.o.C., Stroke Nursing, C.o.E., Prevention, C.o.P.V.D., Stroke, C.: Cardiovascular health: the importance of measuring patient-reported health status: a scientific statement from the American Heart Association. Circulation 127(22), 2233–2249 (2013). doi:https://doi.org/10.1161/CIR.0b013e3182949a2e Rumsfeld, J.S., Alexander, K.P., Goff, D.C., Jr., Graham, M.M., Ho, P.M., Masoudi, F.A., Moser, D.K., Roger, V.L., Slaughter, M.S., Smolderen, K.G., Spertus, J.A., Sullivan, M.D., Treat-Jacobson, D., Zerwic, J.J., American Heart Association Council on Quality of, C., Outcomes Research, C.o.C., Stroke Nursing, C.o.E., Prevention, C.o.P.V.D., Stroke, C.: Cardiovascular health: the importance of measuring patient-reported health status: a scientific statement from the American Heart Association. Circulation 127(22), 2233–2249 (2013). doi:https://​doi.​org/​10.​1161/​CIR.​0b013e3182949a2e​
5.
go back to reference Pharmaceutical Benefits Advisory Committee: Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (PBAC), version 5.0. Available from: https://pbac.pbs.gov.au/. (2016). Pharmaceutical Benefits Advisory Committee: Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (PBAC), version 5.0. Available from: https://​pbac.​pbs.​gov.​au/​. (2016).
12.
go back to reference Richardson J, I., Khan, M., Maxwell, A.: Cross-national comparison of twelve quality of life instruments: MIC paper1: background, questions, instruments. Melbourne, Victoria, Australia: Research Paper76, Centre for Health Economics, Monash University, 2012. Richardson J, I., Khan, M., Maxwell, A.: Cross-national comparison of twelve quality of life instruments: MIC paper1: background, questions, instruments. Melbourne, Victoria, Australia: Research Paper76, Centre for Health Economics, Monash University, 2012.
28.
go back to reference Rabe Hesketh, B., Everitt, B.: A handbook of statistical analyses using Stata (fourth edition). Chapman and Hall/CRC, Boca Raton, Florida (2007) Rabe Hesketh, B., Everitt, B.: A handbook of statistical analyses using Stata (fourth edition). Chapman and Hall/CRC, Boca Raton, Florida (2007)
29.
go back to reference Chen, G., Garcia-Gordillo, M.A., Collado-Mateo, D., del Pozo-Cruz, B., Adsuar, J.C., Cordero-Ferrera, J.M., Abellán-Perpiñán, J.M., Sánchez-Martínez, F.I.: Converting Parkinson-specific scores into health state utilities to assess cost-utility analysis. Patient Patient-Centered Outcomes Res. 11(6), 665–675 (2018). https://doi.org/10.1007/s40271-018-0317-5CrossRef Chen, G., Garcia-Gordillo, M.A., Collado-Mateo, D., del Pozo-Cruz, B., Adsuar, J.C., Cordero-Ferrera, J.M., Abellán-Perpiñán, J.M., Sánchez-Martínez, F.I.: Converting Parkinson-specific scores into health state utilities to assess cost-utility analysis. Patient Patient-Centered Outcomes Res. 11(6), 665–675 (2018). https://​doi.​org/​10.​1007/​s40271-018-0317-5CrossRef
30.
go back to reference Freese J, JS, L.: Regression models for categorical dependent variables using Stata. Austin: Stata. (2006) Freese J, JS, L.: Regression models for categorical dependent variables using Stata. Austin: Stata. (2006)
40.
go back to reference Hays, R.D., Revicki, D.A., Feeny, D., Fayers, P., Spritzer, K.L., Cella, D.: Using Linear Equating to Map PROMIS((R)) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3. Pharmacoeconomics 34(10), 1015–1022 (2016). doi:https://doi.org/10.1007/s40273-016-0408-x Hays, R.D., Revicki, D.A., Feeny, D., Fayers, P., Spritzer, K.L., Cella, D.: Using Linear Equating to Map PROMIS((R)) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3. Pharmacoeconomics 34(10), 1015–1022 (2016). doi:https://​doi.​org/​10.​1007/​s40273-016-0408-x
Metadata
Title
Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique
Authors
Lan Gao
Wei Luo
Utsana Tonmukayakul
Marj Moodie
Gang Chen
Publication date
01-03-2021
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 2/2021
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-020-01259-9

Other articles of this Issue 2/2021

The European Journal of Health Economics 2/2021 Go to the issue