Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences

Crott, Ralph; Briggs, Andrew

doi:10.1007/s10198-010-0233-7

Cited by 99 publications

(94 citation statements)

References 27 publications

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“…For example, the EORTC QLQ30 is a PROM used with patients with any type of tumor. Mappings have been estimated based on samples of patients with breast cancer 37 . Judgements about the suitability of a mapping study in a CEA should be based on an assessment of the differences between the patients or diseases in question.…”

Section: Selection Of a Mapping Model For A Cost-effectiveness Studymentioning

confidence: 99%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

et al. 2017

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ACKNOWLEDGMENTSThe individual contributions of Sorrel Wolowacz and Richard Willke are gratefully acknowledged. We thank all reviewers who commented during our forums at the ISPOR Milan and Amsterdam European Congresses. We especially thank the following individuals who reviewed drafts of the report and submitted written comments. Their feedback has both improved the manuscript and made it an expert consensus ISPOR task force report. Many thanks to Tony Ades, René Allard, Gouri Shankar Bhattacharyya, Lance Brannman, Michael Carter, David Cella, Akriti Chahar, Stephanie Yanjing Chen, Shiva Devarakonda, Salah Ghabri, Linda Gore Martin, Inigo Gorostiza, Thomas Grochtdreis, Michael Hagan, Nadine Hillock, Cynthia Holmes, Shrividya Iyer, Steve Kay, Jeanette Kusel, Ramanath KV, Dawn Lee, Joanna Le niowska, Sophia E. Marsh, Alan Martin, Nicholas Mitsakakis, Sharanya Murty, Nneka Onwudiwe, Guilhem Pietri, G.M. Rabiul Islam, Ghabri Salah, Mihail Samnaliev, Carsten Schousboe, Sarah Shingler, Fatema Turkistani, and Uday Venkat. Finally, many thanks to Theresa Tesoro for her assistance in developing this task force report. 2 AbstractEconomic evaluation conducted in terms of cost per Quality Adjusted Life Year (QALY) provides information that decision makers find useful in many parts of the world. Ideally, clinical studies designed to assess the effectiveness of health technologies would include outcome measures that are directly linked to health utility in order to calculate QALYs. Often this does not happen and, even where it does, clinical studies may be insufficient for a cost-utility assessment. Mapping can solve this problem. It uses an additional dataset to estimate the relationship between outcomes measured in clinical studies and health utility. This bridges the evidence gap between available evidence on the effect of a health technology in one metric and the requirement for decision makers to express it in a different one (QALYs). In 2014, ISPOR established a Good Practices for Outcome Research Task Force for mapping studies. This Task Force Report provides recommendations to analysts undertaking mapping studies, those that use the results in cost utility analysis, and those that need to critically review such studies. The recommendations cover all areas of mapping practice: the selection of datasets for the mapping estimation, model selection and performance assessment, reporting standards, and the use of results including the appropriate reflection of variability and uncertainty. This report is unique because it takes an international perspective, is comprehensive in its coverage of the aspects of mapping practice, and reflects the current state of the art.

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Section: Selection Of a Mapping Model For A Cost-effectiveness Studymentioning

confidence: 99%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

et al. 2017

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“…Ideally, the process of constructing the CS-PBM is supported by the original developers of the questionnaires. This is relevant, for example, to avoid wild growth of value sets (e.g., for the QLQ-C30 now multiple value sets exist derived via mappings [15,[27][28][29]), to further guarantee quality, and to offer support to users of the CS-PBM. Constructing and using a CS-PBM for the purpose of resource allocation could be considered when the following conditions are met: empirical evidence disproves the sensitivity of existing generic instruments, empirical evidence proves the superiority of the condition-specific measure from which the new PBM will be derived, and the derived CS-PBM is shown to be superior to the existing CS-PBM, not just in terms of statistical sensitivity but also in terms of absolute differences.…”

Section: Discussionmentioning

confidence: 99%

Condition-Specific Preference-Based Measures: Benefit or Burden?

Versteegh¹,

Leunis²,

Groot³

et al. 2012

Value in Health

103

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“…Specifically, models tend to underpredict at the upper extreme of the EQ-5D questionnaire scale and overpredict over much of the remainder of the scale but particularly at the lower end [2,13]. Therefore, as Crott and Briggs [14] note, there is currently no agreement on the best method to use. Studies have not applied the same criteria for judging the appropriateness of models, and there are differences in the characteristics of the data sets used in these studies that may influence the findings.…”

Section: Existing Literaturementioning

confidence: 92%

Tails from the Peak District: Adjusted Limited Dependent Variable Mixture Models of EQ-5D Questionnaire Health State Utility Values

2012

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The distribution of the EQ-5D questionnaire is characterized by features that raise statistical challenges. It is well known that standard approaches do not perform well for this reason. This article developed an appropriate method to reflect these features by combining limited dependent variable and mixture modeling and demonstrated superior performance in a rheumatoid arthritis setting. Further refinement of the general framework and testing in other data sets are warranted. Analysis of utility data should apply methods that recognize the distributional features of the data.

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Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences

Abstract: Mapping from QLQ-C30 scores to EQ-5D-derived utilities when only QLQ-C30 data are available has been shown to be possible with good accuracy. Validation of the proposed algorithm in other external clinical datasets should be encouraged.

Cited by 99 publications

References 27 publications

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Condition-Specific Preference-Based Measures: Benefit or Burden?

Tails from the Peak District: Adjusted Limited Dependent Variable Mixture Models of EQ-5D Questionnaire Health State Utility Values

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