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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]

Background: Only one pilot value set (UK) is currently available for the EQ Health and Wellbeing Instrument short version (EQ-HWB-S). As an alternative to preference-weighted scoring, we examined whether a level summary score (LSS) is appropriate for the EQ-HWB-S using Mokken scaling analyses., Methods: Data from patients, carers and the general population collected during the developmental phase of the EQ-HWB-S in Australia, US and UK were used, noting 3 of 9 items have since undergone revision. EQ-HWB-S data fit was examined using R package Mokken scaling's monotone homogeneity model, utilizing the automated item selection procedure (AISP) as well as Loevinger's scaling coefficients for items and the scale (H S ). Manifest monotonicity was assessed by examining whether the cumulative probability for responses at or above each response level did not decrease across the summary score., Results: EQ-HWB-S data were available for 3340 respondents: US = 903, Australia = 514 and UK = 1923. Mean age was 50 ± 18 and 1841 (55%) were female. AISP placed all 9 items of the EQ-HWB-S on a single scale when the lower bound was set to < 0.448. Strong scalability (H S  = 0.561) was found for the EQ-HWB-S as a single scale. Stronger scales were formed by separating the psychosocial items (n = 6, H S  = 0.683) and physical sensation items (n = 3, H S  = 0.713). No violations of monotonicity were found except for the items mobility and daily activities for the subgroups with long-term conditions and UK subjects, respectively., Discussion: As EQ-HWB-S items formed a strong scale and subscales based on Mokken analysis, LSS is a promising weighting-free approach to scoring., (© 2024. The Author(s).)

Objectives: The development of measures such as the EQ-HWB (EQ Health and Wellbeing) requires selection of items. This study explored the psychometric performance of candidate items, testing their validity in patients, social carer users, and carers., Methods: Article and online surveys that included candidate items (N = 64) were conducted in Argentina, Australia, China, Germany, United Kingdom, and the United States. Psychometric assessment on missing data, response distributions, and known group differences was undertaken. Dimensionality was explored using exploratory and confirmatory factor analysis. Poorly fitting items were identified using information functions, and the function of each response category was assessed using category characteristic curves from item response theory (IRT) models. Differential item functioning was tested across key subgroups., Results: There were 4879 respondents (Argentina = 508, Australia = 514, China = 497, Germany = 502, United Kingdom = 1955, United States = 903). Where missing data were allowed, it was low (UK article survey 2.3%; US survey 0.6%). Most items had responses distributed across all levels. Most items could discriminate between groups with known health conditions with moderate to large effect sizes. Items were less able to discriminate across carers. Factor analysis found positive and negative measurement factors alongside the constructs of interest. For most of the countries apart from China, the confirmatory factor analysis model had good fit with some minor modifications. IRT indicated that most items had well-functioning response categories but there was some evidence of differential item functioning in many items., Conclusions: Items performed well in classical psychometric testing and IRT. This large 6-country collaboration provided evidence to inform item selection for the EQ-HWB measure., (Copyright © 2021 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc. All rights reserved.)