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Background: The European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30) is a widely used quality-of-life measure in oncology. The ability to translate QLQ-C30 responses into utility scores would further expand its use in medical decision-making. The aims of this study were to: 1) map QLQ-C30 responses onto patient time trade-off utility scores; and 2) compare a multiattribute approach to a global evaluation approach to modeling utility scores. Methods: Two distinct approaches were applied to data from 1432 cancer patients. The multiattribute approach used psychometric analysis and expert input to select a subset of functioning and symptom scale items for modeling. The second approach focused on global health and quality-of-life items based on a conceptual model. Model selection criteria included parsimony, statistical significance and logical consistency of parameter estimates, predictive accuracy, number of states described, and scale range. Results: The optimal multiattribute model included nine variables for five items from different scales, described 144 unique states, predicted values ranging from 0.63 to 1.00, but it had poor predictive accuracy (cross-validation pseudo- R2 = 0.056). The best-fitting global approach-based model described 24 unique states using eight indicators for two items from one scale (plus a constant) and predicted values ranging from 0.17 to 1.00 (cross-validation pseudo- R2 = 0.127). Conclusions: Multiattribute models produced a greater number of unique predicted values, while global models exhibited more desirable statistical properties and a wider range of values. The recommended models will enable users to predict cancer patients’ utilities from existing and future QLQ-C30 data sets. [ABSTRACT FROM AUTHOR]

Objectives: Clinical trials and community-based studies often include the Center for Epidemiologic Studies-Depression scale (CES-D) as a measure of depression outcome. We compared responses to symptom-related items on the CES-D by depressed stroke and primary-care patients for several purposes: 1) to illustrate the use of Item Response Theory (IRT)-based (Rasch) models for comparing scale functioning across different patient subgroups; and 2) to inform clinicians and outcome researchers about scale functioning and depressive symptomatology in stroke- compared with primary care-based depression. Methods: Two data sources were analyzed, including 32 depressed patients who were 3 months poststroke, and 366 depressed primary-care patients. Presence of depression was based on a CES-D score 16 or higher. Rasch models were used to assess item fit and compare item hierarchies between depressed primary-care and stroke patients. Results: Item hierarchies were similar for poststroke depression and primary care-based depression. Interpersonal disruption items were the most difficult to endorse for both groups. No items misfit the scale in primary-care depression. Items relating to restless sleep, unfriendliness, and crying slightly misfit the scale in stroke patients, that is, may measure a different trait. Differential item functioning (DIF) between the groups was identified for items relating to appetite, restless sleep, crying, and feeling disliked. Conclusions: Results generally supported the use of the CES-D as measure of depression outcome, particularly in primary care-based depression. DIF may imply that slightly different clusters of depressive symptoms are reported by depressed stroke patients compared with primary care, but this is conjectural given the small stroke sample size and the same items have been previously associated with bias in studies of large nonstroke samples. This study found Rasch models to be useful tools to investigate scale performance for different clinical applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]