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Tytuł pozycji:

Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods.

Tytuł :
Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods.
Autorzy :
Hernández Alava M; School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Wailoo A; School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Pudney S; School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Gray L; School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Manca A; Centre for Health Economics, University of York, York, UK.
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Źródło :
Health technology assessment (Winchester, England) [Health Technol Assess] 2020 Jun; Vol. 24 (34), pp. 1-68.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
Język :
English
Imprint Name(s) :
Publication: <2013-> : Southampton, UK : NIHR Journals Library
Original Publication: Winchester, UK : National Co-Ordinating Centre for HTA, c1997-
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Grant Information :
MR/L022575/1 United Kingdom MRC_ Medical Research Council; 15/141/09 United Kingdom DH_ Department of Health
Contributed Indexing :
Keywords: BIOMEDICAL/METHODS*; MODELS, STATISTICAL*; MODELS, THEORETICAL*; OUTCOME ASSESSMENT (HEALTH CARE)/METHODS*; QUALITY-ADJUSTED LIFE-YEARS*; TECHNOLOGY ASSESSMENT*
Local Abstract: [plain-language-summary] Coherent decisions about which health services and treatments to provide rely on economic analysis to weigh potential health benefits against costs. For decisions to be consistent across the whole health service, benefits need to be counted in the same way for patients with different health problems. This is accomplished by using a unit of measurement for treatment outcomes called the quality-adjusted life-year. The best way to calculate quality-adjusted life-years is to ask patients taking part in clinical studies to fill in specially designed questionnaires to describe their health in a simple, standardised way. However, clinical trials often record patient outcomes in different ways, leaving economic analysts without the necessary information to calculate quality-adjusted life-years. A way to overcome this problem (known as ‘statistical mapping’) is to use the available clinical data to predict the responses that would have been made by trial participants to the standard questionnaire. This requires analysis of data from an additional study in which patients have provided both types of outcome data to construct a statistical ‘mapping model’. Mapping is widely used in practice, but it is often based on simple mapping models that in some circumstances systematically mispredict and may consequently give a false picture of the real health benefits of treatments. This is important because it influences decisions about which treatments are available in the NHS; it has real effects on patients, clinicians, industry and the general public. Our objectives are to develop promising new statistical mapping models specifically designed for different clinical contexts and to compare them using patient data in different disease areas. We have also developed an approach for judging the outcome of a mapping study. We find that the new methods work better than existing methods in terms of their ability to fit the data and avoid systematic bias.
Entry Date(s) :
Date Created: 20200703 Latest Revision: 20210112
Update Code :
20210210
PubMed Central ID :
PMC7357250
DOI :
10.3310/hta24340
PMID :
32613941
Czasopismo naukowe
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).

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