Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: an online database and application of the MAPS statement

Dakin, Helen; Abel, Lucy; Burns, Richéal; Yang, Yaling

doi:10.1186/s12955-018-0857-3

Cited by 88 publications

(80 citation statements)

References 36 publications

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“…The predicted utilities in Young et al were derived using a response mapping technique that models the probability of a patient scoring 1, 2, or 3 for each of the five EQ-5D-3 L dimensions, which are then used to calculate the EQ-5D index score rather than predicting the EQ-5D index score directly (as is done in beta-binomial methods in the other two papers). Although this type of mapping is uncommon, it appears to perform well in our validation exercise [10]. In addition, these methods allow for prediction of utility using any country tariff by scoring the predicted responses using the appropriate coefficients.…”

Section: Discussionmentioning

confidence: 90%

“…Three methods were used to identify published mappings algorithms. The first was a search of the Oxford mapping database v6.0 [10] for all mappings between the EORTC-QLQ-C30 and any version of the EQ-5D. The second method involved searching MEDLINE (via PubMed) using the search "mapping" AND "lung" AND "EQ-5D*".…”

Section: Identification and Selection Of Existing Mapping Algorithmsmentioning

confidence: 99%

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The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

et al. 2020

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Background: Mapping algorithms can be used to generate health state utilities when a preference-based instrument is not included in a clinical study. Our aim was to investigate the external validity of published mapping algorithms in non-small cell lung cancer (NSCLC) between the EORTC QLQ-C30 and EQ-5D instruments and to propose methodology for validating any mapping algorithms. Methods: We conducted a targeted literature review to identify published mappings, then applied these to data from the osimertinib clinical trial programme. Performance of the algorithms was evaluated using the mean absolute error, root mean squared error, and graphical techniques for the observed versus predicted EQ-5D utilities. These statistics were also calculated across the range of utility values (as well as ordinary least squares and quantile regression), to investigate how the mappings fitted across all values, not simply around the mean utility. Results: Three algorithms developed in NSCLC were identified. The algorithm based on response mapping (Young et al., 2015) fitted the validation dataset across the range of observed values with similar fit statistics to the original publication (overall MAE of 0.087 vs 0.134). The two algorithms based on beta-binomial models presented a poor fit to both the mean and distribution of utility values (MAE 0.176, 0.178). Conclusions: The validation of mapping algorithms is key to demonstrating their generalisability beyond the original dataset, particularly across the range of plausible utility values (not just the mean)perceived patient similarity being insufficient. The identified algorithm from Young et al. performed well across the range of EORTC scores observed, and thus appears most suitable for use in other studies of NSCLC patients.

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Section: Discussionmentioning

confidence: 90%

Section: Identification and Selection Of Existing Mapping Algorithmsmentioning

confidence: 99%

The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

et al. 2020

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“…23 They estimated a relationship through OLS regression and forward selection. 27,28 That said, a major limitation of mapping algorithms is that generic HU measures often fail to capture many of the unique dimensions outlines in condition-specific quality of life instruments. The variance of the actual EQ-5D-3L scores was 0.0275, while the variance of the predicted HUs was 0.0164.…”

Section: Discussionmentioning

confidence: 99%

“…Mapping models present a promising alternative, with over 200 algorithms having been reported to date. 27,28 That said, a major limitation of mapping algorithms is that generic HU measures often fail to capture many of the unique dimensions outlines in condition-specific quality of life instruments. Even in the EQ5D model, which performed quite well, many disease-specific dimensions were eliminated from the final model (eg, appearance, swallowing, chewing, taste).…”

Section: Discussionmentioning

confidence: 99%

Mapping the University of Washington Quality of life questionnaire onto EQ‐5D and HUI‐3 indices in patients with head and neck cancer

Stephens

Noel

et al. 2019

Head & Neck

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Background: There is no mechanism to predict health utility (HU) values from the University of Washington Quality of Life Questionnaire (UWQoL) scores. We sought to develop a mapping algorithm capable of using UWQoL data to approximate HU scores.Methods: Outpatients with head and neck cancer completed the UWQoL, EQ-5D, and the Health Utilities Index-Mark 3 (HUI-3). Results of the UWQoL were mapped onto both EQ-5D and HUI-3 scores using ordinary least-squares regression models. Two-part models were explored. The predictive power of the model was assessed using 10-fold cross-validation.Results: A total of 209 patients were recruited. The reduced model converting UWQoL data into EQ-5D scores performed best (adjusted R 2 = 0.628, root mean square error = 0.076). Both models demonstrated construct validity by discriminating between clinical indices of disease severity. Conclusions:The abovementioned algorithms enable researchers to perform health economic evaluations with existing UWQoL data in cases where prospectively collected HU values are not available. K E Y W O R D Scross-walking, EQ-5D, head and neck cancer (HNC), health state utility values (HSUVs), health utilities index mark 3, health-related quality of life (HRQoL), mapping algorithms, multiattribute utility instruments (MAUIs), preference-based measures, QALYs, University of Washington Quality of life questionnaire

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“…If clinical or condition-specific measures relevant to the intervention of interest are routinely collected which could be used for CEA, then a 'mapping' or 'cross-walk' algorithm may exist to allow the statistical prediction of utility values from that measure to conduct CUA. The purpose and procedures of statistical mapping are described by Longworth and Rowen [78], with a systematic review of mapping studies by Mukuria et al [79], and an online database of mapping studies also currently available (HERC database of mapping studies [80]: www.herc.ox.ac.uk/downl oads/herc-datab aseof-mappi ng-studi es).…”

Section: Routine Data For Estimating Resource Use Costs and Non-monementioning

confidence: 99%

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

2020

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This article provides an educational review covering the consideration of conducting 'value for money' analyses as part of non-randomised study designs including service evaluations. These evaluations represent a vehicle for producing evidence such as value for money of a care intervention or service delivery model. Decision makers including charities and local and national governing bodies often rely on evidence from non-randomised data and service evaluations to inform their resource allocation decision-making. However, as randomised data obtained from randomised controlled trials are considered the 'gold standard' for assessing causation, the use of this alternative vehicle for producing an evidence base requires careful consideration. We refer to value for money analyses, but reflect on methods associated with economic evaluations as a form of analysis used to inform resource allocation decision-making alongside a finite budget. Not all forms of value for money analysis are considered a full economic evaluation with implications for the information provided to decision makers. The type of value for money analysis to be conducted requires considerations such as the outcome(s) of interest, study design, statistical methods to control for confounding and bias, and how to quantify and describe uncertainty and opportunity costs to decision makers in any resulting value for money estimates. Service evaluations as vehicles for producing evidence present different challenges to analysts than what is commonly associated with research, randomised controlled trials and health technology appraisals, requiring specific study design and analytic considerations. This educational review describes and discusses these considerations, as overlooking them could affect the information provided to decision makers who may make an 'ill-informed' decision based on 'poor' or 'inaccurate' information with long-term implications. We make direct comparisons between randomised controlled trials relative to non-randomised data as vehicles for assessing causation; given 'gold standard' randomised controlled trials have limitations. Although we use UK-based decision makers as examples, we reflect on the needs of decision makers internationally for evidence-based decision-making specific to resource allocation. We make recommendations based on the experiences of the authors in the UK, reflecting on the wide variety of methods available, used as documented in the empirical literature. These methods may not have been fully considered relevant to non-randomised study designs and/or service evaluations, but could improve and aid the analysis conducted to inform the relevant value for money decision problem.

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Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: an online database and application of the MAPS statement

Cited by 88 publications

References 36 publications

The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

Mapping the University of Washington Quality of life questionnaire onto EQ‐5D and HUI‐3 indices in patients with head and neck cancer

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

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