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This study assessed the content validity of generic and condition-specific preference-based measures (PBMs) with patients treated for cancer, evaluated against 10 Consensus-Based Standards for the Selection of Health Measurement Instruments criteria for good content validity, to best inform measurement strategies regarding the use of PBMs in oncology development programs and real-world applications. Individual, semistructured interviews were conducted with patients who received drug treatment for cancer in the United Kingdom (n = 47) and the United States (n = 49). During the interview, patients completed 3 generic PBMs (EQ-5D-5L, EuroQol Health and Wellbeing measure–Short Form, Château Santé Base) and 2 condition-specific PBMs (Quality of Life Utility-Core 10 Dimension, Functional Assessment of Cancer Therapy Eight Dimension [FACT-8D]). Interviews were conducted via teleconference, audio recorded, and transcribed verbatim. Transcripts were coded using thematic and content analysis methods. Condition-specific measures were evaluated as having better relevancy than generic PBMs. Overall, the FACT-8D was evaluated as holding the best content validity in terms of relevancy, and the EuroQol Health and Wellbeing measure–Short Form received the most favorable evaluation of relevancy for generic PBMs. All measures demonstrated comparable comprehensiveness, with all suggested by patients to be missing concepts. The EQ-5D-5L was evaluated best in terms of comprehensibility. This was followed by the Quality of Life Utility-Core 10 Dimension and FACT-8D; both received similar evaluations. All measures were generally seen by patients as adequate in capturing appropriate aspects of health-related quality of life for measuring cancer outcomes, although together condition-specific measures were evaluated as having better relevancy than generic PBMs. Further health-related quality of life instrument development is encouraged, particularly with regard to the longer-term detrimental impacts of cancer and treatment side effects. Other developments could include new cancer-specific tools inclusive of conventional health items, treatment impacts, and psychological items. • Generic preference-based measures (PBMs) are recommended to assess patient-reported outcomes across interventions and to allow for comparative assessments across unrelated health conditions and interventions. Generic PBMs may exclude dimensions relevant to specific conditions, whereas condition-specific PBMs can contain items related to specific symptoms. Recent work has identified several concepts that are relevant to health-related quality of life and psychological wellbeing, which are not measured by existing outcome measures. • Patients who had received drug treatment for cancer in the United Kingdom and the United States evaluated 3 generic and 2 condition-specific PBMs during qualitative interviews. All measures demonstrated comparable comprehensiveness, missing concepts considered by patients as important to measure for their cancer and cancer treatment experiences. Language and format were frequently challenged, with patients suggesting changes to multiple items across all measures. Recall periods were frequently raised as problematic because of the substantial variation in cancer symptoms within a given day or week. • Given the burden of cancer symptoms and drug treatment effects, capturing all relevant outcomes of people with cancer is necessary to inform accurate decision making for cancer technologies. Modification of existing measures should be considered with regard to measurement gaps identified. New cancer-specific tools could be considered, inclusive of conventional health items, treatment impacts, and psychological items. These developments have a large potential for impact given the high proportions of oncology health technology assessment submissions. [ABSTRACT FROM AUTHOR]

Objectives: Existing measures for estimating quality-adjusted life-years are mostly limited to health-related quality of life. This article presents an overview of the development the EQ-HWB (EQ Health and Wellbeing), which is a measure that encompasses health and wellbeing.Methods: Stages: (1) Establishing domains through reviews of the qualitative literature informed by a conceptual framework. (2) Generation and selection of items to cover the domains. (3) Face validation of these items through qualitative interviews with 168 patients, social care users, general population, and carers across 6 countries (Argentina, Australia, China, Germany, United Kingdom, United States). (4) Extensive psychometric testing of candidate items (using classical, factor analysis, and item response theory methods) on > 4000 respondents in the 6 countries. Stakeholders were consulted throughout.Results: A total of 32 subdomains grouped into 7 high-level domains were identified from the qualitative literature and 97 items generated to cover them. Face validation eliminated 36 items, modified 14, and added 3. Psychometric testing of 64 items found little difference in missing data or problems with response distribution, the conceptual model was confirmed except in China, and most items performed well in the item response theory in all countries. Evidence was presented to stakeholders in 2 rounds of consultation to inform the final selection of items for the EQ-HWB (25-item) and the short version of EQ-HWB (9-items).Conclusions: EQ-HWB measures have been developed internationally for evaluating interventions in health, public health, and social care including the impact on patients, social care users, and carers. [ABSTRACT FROM AUTHOR]

Abstract: Background: A five-level version of the EuroQol five-dimensional (EQ-5D) descriptive system (EQ-5D-5L) has been developed, but value sets based on preferences directly elicited from representative general population samples are not yet available. The objective of this study was to develop values sets for the EQ-5D-5L by means of a mapping (“crosswalk”) approach to the currently available three-level version of the EQ-5D (EQ-5D-3L) values sets. Methods: The EQ-5D-3L and EQ-5D-5L descriptive systems were coadministered to respondents with conditions of varying severity to ensure a broad range of levels of health across EQ-5D questionnaire dimensions. We explored four models to generate value sets for the EQ-5D-5L: linear regression, nonparametric statistics, ordered logistic regression, and item-response theory. Criteria for the preferred model included theoretical background, statistical fit, predictive power, and parsimony. Results: A total of 3691 respondents were included. All models had similar fit statistics. Predictive power was slightly better for the nonparametric and ordered logistic regression models. In considering all criteria, the nonparametric model was selected as most suitable for generating values for the EQ-5D-5L. Conclusions: The nonparametric model was preferred for its simplicity while performing similarly to the other models. Being independent of the value set that is used, it can be applied to transform any EQ-5D-3L value set into EQ-5D-5L index values. Strengths of this approach include compatibility with three-level value sets. A limitation of any crosswalk is that the range of index values is restricted to the range of the EQ-5D-3L value sets. [Copyright &y& Elsevier]