Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer

Abstract Background The types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments...

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Autores principales:	Laura A. Gray, Monica Hernandez Alava, Allan J. Wailoo
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	QLQ-C30 EQ-5D-3L Utility mapping Mixture models ALDVMM Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/45189f29fd5d41dfb4a1065fe8a3e5c4
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spelling	oai:doaj.org-article:45189f29fd5d41dfb4a1065fe8a3e5c42021-11-21T12:30:33ZMapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer10.1186/s12885-021-08964-51471-2407https://doaj.org/article/45189f29fd5d41dfb4a1065fe8a3e5c42021-11-01T00:00:00Zhttps://doi.org/10.1186/s12885-021-08964-5https://doaj.org/toc/1471-2407Abstract Background The types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation. Methods We use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics. Results A four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users. Conclusion A four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.Laura A. GrayMonica Hernandez AlavaAllan J. WailooBMCarticleQLQ-C30EQ-5D-3LUtility mappingMixture modelsALDVMMNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENBMC Cancer, Vol 21, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	QLQ-C30 EQ-5D-3L Utility mapping Mixture models ALDVMM Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	QLQ-C30 EQ-5D-3L Utility mapping Mixture models ALDVMM Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Laura A. Gray Monica Hernandez Alava Allan J. Wailoo Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
description	Abstract Background The types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation. Methods We use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics. Results A four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users. Conclusion A four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.
format	article
author	Laura A. Gray Monica Hernandez Alava Allan J. Wailoo
author_facet	Laura A. Gray Monica Hernandez Alava Allan J. Wailoo
author_sort	Laura A. Gray
title	Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
title_short	Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
title_full	Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
title_fullStr	Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
title_full_unstemmed	Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer
title_sort	mapping the eortc qlq-c30 to eq-5d-3l in patients with breast cancer
publisher	BMC
publishDate	2021
url	https://doaj.org/article/45189f29fd5d41dfb4a1065fe8a3e5c4
work_keys_str_mv	AT lauraagray mappingtheeortcqlqc30toeq5d3linpatientswithbreastcancer AT monicahernandezalava mappingtheeortcqlqc30toeq5d3linpatientswithbreastcancer AT allanjwailoo mappingtheeortcqlqc30toeq5d3linpatientswithbreastcancer
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Mapping the EORTC QLQ-C30 to EQ-5D-3L in patients with breast cancer

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