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Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods.
Health Technol Assess. 2020 Jun; 24(34):1-68.HT

Abstract

BACKGROUND

Cost-effectiveness analysis using quality-adjusted life-years as the measure of health benefit is commonly used to aid decision-makers. Clinical studies often do not include preference-based measures that allow the calculation of quality-adjusted life-years, or the data are insufficient. 'Mapping' can bridge this evidence gap; it entails estimating the relationship between outcomes measured in clinical studies and the required preference-based measures using a different data set. However, many methods for mapping yield biased results, distorting cost-effectiveness estimates.

OBJECTIVES

Develop existing and new methods for mapping; test their performance in case studies spanning different preference-based measures; and develop methods for mapping between preference-based measures.

DATA SOURCES

Fifteen data sets for mapping from non-preference-based measures to preference-based measures for patients with head injury, breast cancer, asthma, heart disease, knee surgery and varicose veins were used. Four preference-based measures were covered: the EuroQoL-5 Dimensions, three-level version (n = 11), EuroQoL-5 Dimensions, five-level version (n = 2), Short Form questionnaire-6 Dimensions (n = 1) and Health Utility Index Mark 3 (n = 1). Sample sizes ranged from 852 to 136,327. For mapping between generic preference-based measures, data from FORWARD, the National Databank for Rheumatic Diseases (which includes the EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, in its 2011 wave), were used.

MAIN METHODS DEVELOPED

Mixture-model-based approaches for direct mapping, in which the dependent variable is the health utility value, including adaptations of methods developed to model the EuroQoL-5 Dimensions, three-level version, and beta regression mixtures, were developed, as were indirect methods, in which responses to the descriptive systems are modelled, for consistent multidirectional mapping between preference-based measures. A highly flexible approach was designed, using copulas to specify the bivariate distribution of each pair of EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, responses.

RESULTS

A range of criteria for assessing model performance is proposed. Theoretically, linear regression is inappropriate for mapping. Case studies confirm this. Flexible, direct mapping methods, based on different variants of mixture models with appropriate underlying distributions, perform very well for all preference-based measures. The precise form is important. Case studies show that a minimum of three components are required. Covariates representing disease severity are required as predictors of component membership. Beta-based mixtures perform similarly to the bespoke mixture approaches but necessitate detailed consideration of the number and location of probability masses. The flexible, bi-directional indirect approach performs well for testing differences between preference-based measures.

LIMITATIONS

Case studies drew heavily on EuroQoL-5 Dimensions. Indirect methods could not be undertaken for several case studies because of a lack of coverage. These methods will often be unfeasible for preference-based measures with complex descriptive systems.

CONCLUSIONS

Mapping requires appropriate methods to yield reliable results. Evidence shows that widely used methods such as linear regression are inappropriate. More flexible methods developed specifically for mapping show that close-fitting results can be achieved. Approaches based on mixture models are appropriate for all preference-based measures. Some features are universally required (such as the minimum number of components) but others must be assessed on a case-by-case basis (such as the location and number of probability mass points).

FUTURE RESEARCH PRIORITIES

Further research is recommended on (1) the use of the monotonicity concept, (2) the mismatch of trial and mapping distributions and measurement error and (3) the development of indirect methods drawing on methods developed for mapping between preference-based measures.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 34. See the NIHR Journals Library website for further project information. This project was also funded by a Medical Research Council grant (MR/L022575/1).

Authors+Show Affiliations

School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.Centre for Health Economics, University of York, York, UK.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32613941

Citation

Hernández Alava, Mónica, et al. "Mapping Clinical Outcomes to Generic Preference-based Outcome Measures: Development and Comparison of Methods." Health Technology Assessment (Winchester, England), vol. 24, no. 34, 2020, pp. 1-68.
Hernández Alava M, Wailoo A, Pudney S, et al. Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods. Health Technol Assess. 2020;24(34):1-68.
Hernández Alava, M., Wailoo, A., Pudney, S., Gray, L., & Manca, A. (2020). Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods. Health Technology Assessment (Winchester, England), 24(34), 1-68. https://doi.org/10.3310/hta24340
Hernández Alava M, et al. Mapping Clinical Outcomes to Generic Preference-based Outcome Measures: Development and Comparison of Methods. Health Technol Assess. 2020;24(34):1-68. PubMed PMID: 32613941.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods. AU - Hernández Alava,Mónica, AU - Wailoo,Allan, AU - Pudney,Stephen, AU - Gray,Laura, AU - Manca,Andrea, PY - 2020/7/3/entrez PY - 2020/7/3/pubmed PY - 2020/7/3/medline KW - BIOMEDICAL/METHODS KW - MODELS, STATISTICAL KW - MODELS, THEORETICAL KW - OUTCOME ASSESSMENT (HEALTH CARE)/METHODS KW - QUALITY-ADJUSTED LIFE-YEARS KW - TECHNOLOGY ASSESSMENT SP - 1 EP - 68 JF - Health technology assessment (Winchester, England) JO - Health Technol Assess VL - 24 IS - 34 N2 - BACKGROUND: Cost-effectiveness analysis using quality-adjusted life-years as the measure of health benefit is commonly used to aid decision-makers. Clinical studies often do not include preference-based measures that allow the calculation of quality-adjusted life-years, or the data are insufficient. 'Mapping' can bridge this evidence gap; it entails estimating the relationship between outcomes measured in clinical studies and the required preference-based measures using a different data set. However, many methods for mapping yield biased results, distorting cost-effectiveness estimates. OBJECTIVES: Develop existing and new methods for mapping; test their performance in case studies spanning different preference-based measures; and develop methods for mapping between preference-based measures. DATA SOURCES: Fifteen data sets for mapping from non-preference-based measures to preference-based measures for patients with head injury, breast cancer, asthma, heart disease, knee surgery and varicose veins were used. Four preference-based measures were covered: the EuroQoL-5 Dimensions, three-level version (n = 11), EuroQoL-5 Dimensions, five-level version (n = 2), Short Form questionnaire-6 Dimensions (n = 1) and Health Utility Index Mark 3 (n = 1). Sample sizes ranged from 852 to 136,327. For mapping between generic preference-based measures, data from FORWARD, the National Databank for Rheumatic Diseases (which includes the EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, in its 2011 wave), were used. MAIN METHODS DEVELOPED: Mixture-model-based approaches for direct mapping, in which the dependent variable is the health utility value, including adaptations of methods developed to model the EuroQoL-5 Dimensions, three-level version, and beta regression mixtures, were developed, as were indirect methods, in which responses to the descriptive systems are modelled, for consistent multidirectional mapping between preference-based measures. A highly flexible approach was designed, using copulas to specify the bivariate distribution of each pair of EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, responses. RESULTS: A range of criteria for assessing model performance is proposed. Theoretically, linear regression is inappropriate for mapping. Case studies confirm this. Flexible, direct mapping methods, based on different variants of mixture models with appropriate underlying distributions, perform very well for all preference-based measures. The precise form is important. Case studies show that a minimum of three components are required. Covariates representing disease severity are required as predictors of component membership. Beta-based mixtures perform similarly to the bespoke mixture approaches but necessitate detailed consideration of the number and location of probability masses. The flexible, bi-directional indirect approach performs well for testing differences between preference-based measures. LIMITATIONS: Case studies drew heavily on EuroQoL-5 Dimensions. Indirect methods could not be undertaken for several case studies because of a lack of coverage. These methods will often be unfeasible for preference-based measures with complex descriptive systems. CONCLUSIONS: Mapping requires appropriate methods to yield reliable results. Evidence shows that widely used methods such as linear regression are inappropriate. More flexible methods developed specifically for mapping show that close-fitting results can be achieved. Approaches based on mixture models are appropriate for all preference-based measures. Some features are universally required (such as the minimum number of components) but others must be assessed on a case-by-case basis (such as the location and number of probability mass points). FUTURE RESEARCH PRIORITIES: Further research is recommended on (1) the use of the monotonicity concept, (2) the mismatch of trial and mapping distributions and measurement error and (3) the development of indirect methods drawing on methods developed for mapping between preference-based measures. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 34. See the NIHR Journals Library website for further project information. This project was also funded by a Medical Research Council grant (MR/L022575/1). SN - 2046-4924 UR - https://www.unboundmedicine.com/medline/citation/32613941/Mapping_clinical_outcomes_to_generic_preference-based_outcome_measures:_development_and_comparison_of_methods L2 - https://doi.org/10.3310/hta24340 DB - PRIME DP - Unbound Medicine ER -
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