Search

Purpose The objective of this study was to compare the health-related quality of life (HRQL) burden across different types of advanced cancer with the use of 2 widely used patient-reported outcome measures, the generic EQ-5D and the cancer-specific Functional Assessment of Cancer Therapy-General (FACT-G). Methods Patients with advanced cancer of the bladder, brain, breast, colon/rectum, head/neck, hepatobiliary tract/pancreas, kidney, lung, lymphoma, ovary, or prostate completed the EQ-5D and FACT-G. HRQL domains and summary scores were compared across types of cancer, using regression models to adjust for age and sex. Findings Approximately 50 patients with each type of cancer were recruited (total, 534 patients). According to EQ-5D dimensions, the highest proportion of problems was associated with prostate (mobility); prostate and kidney (self-care); head/neck, bladder, and lung (usual activities); breast, head/neck, and lung (pain/discomfort); and lymphoma and lung (anxiety/depression) cancers. EQ-5D visual analogue scale scores were lowest for breast and head/neck cancers; US index-based scores were lowest for breast and lung cancers and highest for brain cancer. For the FACT-G, physical well-being scores were lowest for head/neck, hepatobiliary, and kidney cancers; emotional well-being scores were lowest for hepatobiliary cancer, and functional well-being scores were lowest for head/neck, hepatobiliary, and bladder cancers. Implications The overall and dimension-specific HRQL burden of advanced cancer depended on the type of cancer. Such results may aid clinicians and patients in better understanding of how cancer site can affect HRQL and functioning in different ways. [ABSTRACT FROM AUTHOR]

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]

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]

Objective: The goal of this study was to estimate an algorithm to convert responses to the Functional Assessment of Cancer Therapy—General (FACT-G) to time trade-off (TTO) utilities based on utilities for current health elicited from cancer patients. Methods: Data for 1433 cancer patients were randomly separated into construction and validation samples. Four FACT-G questions were selected for inclusion based on correlation with Eastern Clinical Oncology Group—Performance Status (ECOG-PS) scores and TTO utilities. Item response theory was used to collapse response categories. Ordinary least squares regression with the constant constrained to one was used to estimate the algorithm. Results: The algorithm estimated mean utility for the full validation sample within three points of observed mean utility (0.805 vs. 0.832, P < 0.01). Mean utilities were wellpredicted (mean absolute difference < 0.03, P > 0.05) for most subgroups defined by ECOG-PS and Short Form-36 physical functioning scores, and responses to the FACT-G overall quality of life item. Nevertheless, the algorithm systematically overpredicted utilities for poorer health states. Conclusions: A FACT-G-based algorithm of cancer patient utilities was developed that estimates group mean utility scores with accuracy comparable to other indirect preference-based measures of health-related quality of life. Patient-based preferences for health outcomes of cancer treatment may be useful in multiple situations, such as managing resources within cancer centers and in understanding health states preferences among cancer experienced patients before and after treatment. [ABSTRACT FROM AUTHOR]