Dataset Information

Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

ABSTRACT: BACKGROUND:The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required. OBJECTIVE:The aim of this study was to provide Australian utility weights for the QLU-C10D. METHODS:An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy. RESULTS:A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death. CONCLUSIONS:This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

SUBMITTER: King MT

PROVIDER: S-EPMC5805814 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

King Madeleine T MT Viney Rosalie R Simon Pickard A A Rowen Donna D Aaronson Neil K NK Brazier John E JE Cella David D Costa Daniel S J DSJ Fayers Peter M PM Kemmler Georg G McTaggart-Cowen Helen H Mercieca-Bebber Rebecca R Peacock Stuart S Street Deborah J DJ Young Tracey A TA Norman Richard R

PharmacoEconomics 20180201 2

<h4>Background</h4>The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required.<h4>Objective</h4>The aim of this study was to provide Australian utili ...[more]

PMID: 29270835

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Project description:BackgroundThe European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility-Core 10 Dimensions (QLU-C10D) is a novel cancer-specific preference-based measure (PBM) for which value sets are being developed for an increasing number of countries. This is done by obtaining health preferences from the respective general population. There is an ongoing discussion if instead patients suffering from the disease in question should be asked for their preferences. We used the QLU-C10D valuation survey, originally designed for use in the general population, in a sample of cancer patients in Austria to assess the methodology's acceptability and applicability in this target group before obtaining QLU-C10D patient preferences.MethodsThe core of the QLU-C10D valuation survey is a discrete choice experiment in which respondents are asked to give preferences for certain health states (described by a relatively large number of 10 quality of life domains) and an associated survival time. They therewith are asked to trade off quality of life against life time. As this might be a very burdensome task for cancer patients undergoing treatment, a cognitive interview was conducted in a pilot sample to assess burden and potential additional needs for explanation in order to be able to use the DCE for the development of QLU-C10D patient preferences. In addition, responses to general feedback questions on the survey were compared against responses from a matched control group from the already completed Austrian general population valuation survey.ResultsWe included 48 patients (mean age 59.9 years; 46% female). In the cognitive interview, the majority indicated that their experience with the survey was positive (85%) and overall clarity as good (90%). In response to the general feedback questions, patients rated the presentation of the health states less clear than matched controls (p = 0.008). There was no difference between patients and the general population concerning the difficulty in choosing between the health states (p = 0.344).ConclusionDespite the relatively large number of DCE domains the survey was manageable for patients and allows going on with the QLU-C10D patient valuation study.

Project description:IntroductionWhen determining if changes on patient-reported outcome (PRO) scores in clinical trials convey a meaningful treatment benefit, statistical significance tests alone may not communicate the patient perspective. Appraising within-patient changes on PRO scores against established thresholds can determine if improvements or deteriorations experienced by individuals are meaningful. To evaluate the appropriateness of thresholds for interpreting meaningful improvements and deterioration within individuals on the European Organisation for Research and Treatment of Cancer (EORTC) 30-item core instrument (QLQ-C30) and 13-item lung cancer module (QLQ-LC13), a series of psychometric methods were applied to data from a phase III randomized controlled clinical trial in non-small cell lung cancer.MethodsAnchor-based methods of empirical cumulative distribution functions and classification statistics were employed using change scores from Baseline to Week 7 using changes on the QLQ-C30 Global Health Status item as an anchor. Distribution-based methods of one-half standard deviation and standard error of measurement identified the minimum amount of change each domain score can reliably measure.ResultsWhile the correlations between the domain scores and the anchor item were modest in size (i.e., r ≥ 0.30 for only 5 of 24 domains), consideration of multiple methods along with the magnitude of possible step changes on the score allowed for patterns to emerge. The triangulation process planned a priori resulted in different methods being the source for different domain scores. Absolute values of the proposed thresholds ranged from 11.11 to 33.33, and all resulted in the same classifications for all EORTC domains, except QLQ-C30 Fatigue, as would the 10-point threshold that is traditionally used.ConclusionThis study confirms the appropriateness of the 10-point EORTC score threshold generally used by the field for interpreting within-patient changes, but the thresholds proposed from this study enhance interpretability by corresponding to only observable locations along the domain score scale.

Project description:BackgroundIn oncology, health-related quality of life (HRQoL) data are often collected using disease-specific patient questionnaires while generic, patient-level utility data required for health economic modeling are often not collected.MethodsWe developed a mapping algorithm for multiple myeloma that relates HRQoL scores from the European Organization for Research and Treatment of Cancer (EORTC) questionnaires QLQ-C30 and QLQ-MY20 to a utility value from the European QoL-5 Dimensions (EQ-5D) questionnaire. Data were obtained from 154 multiple myeloma patients who had participated in a multicenter cohort study in the UK or Germany. All three questionnaires were administered at a single time point. Scores from all 19 domains of the QLQ-C30 and QLQ-MY20 instruments were univariately tested against EQ-5D values and retained in a multivariate regression model if statistically significant. A 10-fold cross-validation model selection method was also used as an alternative testing means. Two models were developed: one based on QLQ-C30 plus QLQ-MY20 scores and one based on QLQ-C30 scores alone. Adjusted R-squared, correlation coefficients, and plots of observed versus predicted EQ-5D values were presented for both models.ResultsMapping revealed that Global Health Status/QoL, Physical Functioning, Pain, and Insomnia were significant predictors of EQ-5D utility values. Similar results were observed when QLQ-MY20 scores were excluded from the model, except that Emotional Functioning and became a significant predictor and Insomnia was no longer a significant predictor. Adjusted R-squared values were of similar magnitude with or without inclusion of QLQ-MY20 scores (0.70 and 0.69, respectively), suggesting that the EORTC QLQ-MY20 adds little in terms of predicting utility values in multiple myeloma.ConclusionsThis algorithm successfully mapped EORTC HRQoL data onto EQ-5D utility in patients with multiple myeloma. Current mapping will aid in the analysis of cost-effectiveness of novel therapies for this indication.

Dataset Information

Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

Publications

Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

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