Japanese value set for the EORTC QLU-C10D: A multi-attribute utility instrument based on the EORTC QLQ-C30 cancer-specific quality-of-life questionnaire

PURPOSE

This study aimed to develop a Japanese value set for the EORTC QLU-C10D, a multi-attribute utility measure derived from the cancer-specific health-related quality-of-life (HRQL) questionnaire, the EORTC QLQ-C30. The QLU-C10D contains ten HRQL dimensions: physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, and bowel problems.

METHODS

Quota sampling of a Japanese online panel was used to achieve representativeness of the Japanese general population by sex and age (≥ 18 years). The valuation method was an online discrete choice experiment. Each participant considered 16 choice pairs, randomly assigned from 960 choice pairs. Each pair included two QLU-C10D health states and life expectancy. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Preference weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy.

RESULTS

A total of 2809 eligible panel members consented, 2662/2809 (95%) completed at least one choice pair, and 2435/2662 (91%) completed all choice pairs. Within dimensions, preference weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Intermediate utility weights were associated with social functioning and nausea; the remaining symptoms and emotional functioning were associated with smaller utility decrements. The value of the worst health state was - 0.221, lower than that seen in most other existing QLU-C10D country-specific value sets.

CONCLUSIONS

The Japan-specific QLU-C10D value set is suitable for evaluating the cost and utility of oncology treatments for Japanese health technology assessment and decision-making.

United States Value Set for the Functional Assessment of Cancer Therapy-General Eight Dimensions (FACT-8D), a Cancer-Specific Preference-Based Quality of Life Instrument

OBJECTIVES

To develop a value set reflecting the United States (US) general population's preferences for health states described by the Functional Assessment of Cancer Therapy (FACT) eight-dimensions preference-based multi-attribute utility instrument (FACT-8D), derived from the FACT-General cancer-specific health-related quality-of-life (HRQL) questionnaire.

METHODS

A US online panel was quota-sampled to achieve a general population sample representative by sex, age (≥ 18 years), race and ethnicity. A discrete choice experiment (DCE) was used to value health states. The valuation task involved choosing between pairs of health states (choice-sets) described by varying levels of the FACT-8D HRQL dimensions and survival (life-years). The DCE included 100 choice-sets; each respondent was randomly allocated 16 choice-sets. Data were analysed using conditional logit regression parameterized to fit the quality-adjusted life-year framework, weighted for sociodemographic variables that were non-representative of the US general population. Preference weights were calculated as the ratio of HRQL-level coefficients to the survival coefficient.

RESULTS

2562 panel members opted in, 2462 (96%) completed at least one choice-set and 2357 (92%) completed 16 choice-sets. Pain and nausea were associated with the largest utility weights, work and sleep had more moderate utility weights, and sadness, worry and support had the smallest utility weights. Within dimensions, more severe HRQL levels were generally associated with larger weights. A preference-weighting algorithm to estimate US utilities from responses to the FACT-General questionnaire was generated. The worst health state's value was -0.33.

CONCLUSIONS

This value set provides US population utilities for health states defined by the FACT-8D for use in evaluating oncology treatments.

The FACT-8D, a new cancer-specific utility algorithm based on the Functional Assessment of Cancer Therapies-General (FACT-G): a Canadian valuation study

Introduction

Utility instruments are used to assess patients’ health-related quality of life for cost-utility analysis (CUA). However, for cancer patients, the dimensions of generic utility instruments may not capture all the information relevant to the impact of cancer. Cancer-specific utilities provide a useful alternative. Under the auspices of the Multi-Attribute Utility in Cancer Consortium, a cancer-specific utility algorithm was derived from the FACT-G. The new FACT-8D contains eight dimensions: pain, fatigue, nausea, sleep, work, support from family/friends, sadness, and worry health will get worse. The aim of the study was to obtain a Canadian value set for the FACT-8D.

Methods

A discrete choice experiment was administered to a Canadian general population online panel, quota sampled by age, sex, and province/territory of residence. Respondents provided responses to 16 choice sets. Each choice set consisted of two health states described by the FACT-8D dimensions plus an attribute representing survival duration. Sample weights were applied and the responses were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life year framework. The results were converted into utility weights by evaluating the marginal rate of substitution between each level of each FACT-8D dimension with respect to duration.

Results

2228 individuals were recruited. The analysis dataset included n = 1582 individuals, who completed at least one choice set; of which, n = 1501 completed all choice sets. After constraining to ensure monotonicity in the utility function, the largest decrements were for the highest levels of pain (− 0.38), nausea (− 0.30), and problems doing work (− 0.23). The decrements of the remaining dimensions ranged from − 0.08 to − 0.18 for their highest levels. The utility of the worst possible health state was defined as − 0.65, considerably worse than dead.

Conclusions

The largest impacts on utility included three generic dimensions (i.e., pain, support, and work) and nausea, a symptom caused by cancer (e.g., brain tumours, gastrointestinal tumours, malignant bowel obstruction) and by common treatments (e.g., chemotherapy, radiotherapy, opioid analgesics). This may make the FACT-8D more informative for CUA evaluating in many cancer contexts, an assertion that must now be tested empirically in head-to-head comparisons with generic utility measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-022-02002-z.

Economic evaluations for intensive care unit randomised clinical trials in Australia and New Zealand: Practical recommendations for researchers

OBJECTIVES

Economic evaluations of intensive care unit (ICU) interventions have specific considerations, including how to cost ICU stays and accurately measure quality of life in survivors. The aim of this article was to develop best practice recommendations for economic evaluations alongside future ICU randomised controlled trials (RCTs).

REVIEW METHODS

We collated our experience based on expert consensus across several recent economic evaluations to provide best-practice, practical recommendations for researchers conducting economic evaluations alongside RCTs in the ICU. Recommendations were structured according to the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Consolidated Health Economic Evaluation Reporting Standards (CHEERS) Task Force Report.

RESULTS

We discuss recommendations across the components of economic evaluations, including: types of economic evaluation, the population and sample size, study perspective, comparators, time horizon, choice of health outcomes, measurement of effectiveness, measurement and valuation of quality of life, estimating resources and costs, analytical methods, and the increment cost-effectiveness ratio. We also provide future directions for research with regard to developing more robust economic evaluations for the ICU.

CONCLUSION

Economic evaluations should be built alongside ICU RCTs and should be designed a priori using appropriate follow-up and data collection to capture patient-relevant outcomes. Further work is needed to improve the quality of data available for linkage in Australia as well as developing costing methods for the ICU and appropriate quality of life measurements.

Responsiveness and convergent validity of QLU-C10D and EQ-5D-3L in assessing short-term quality of life following esophagectomy

Aim

This study assessed the responsiveness and convergent validity of two preference-based measures; the newly developed cancer-specific EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D) relative to the generic three-level version of the EuroQol 5 dimensions (EQ-5D-3L) in evaluating short-term health related quality of life (HRQoL) outcomes after esophagectomy.

Methods

Participants were enrolled in a multicentre randomised controlled trial to determine the impact of preoperative and postoperative immunonutrition versus standard nutrition in patients with esophageal cancer. HRQoL was assessed seven days before and 42 days after esophagectomy. Standardized Response Mean and Effect Size were calculated to assess responsiveness. Ceiling effects for each dimension were calculated as the proportion of the best level responses for that dimension at follow-up/post-operatively. Convergent validity was assessed using Spearman’s correlation and the level of agreement was explored using Bland–Altman plots.

Results

Data from 164 respondents (mean age: 63 years, 81% male) were analysed. HRQoL significantly reduced on both measures with large effect sizes (> 0.80), and a greater mean difference (0.29 compared to 0.16) on QLU-C10D. Both measures had ceiling effects (> 15%) on all dimensions at baseline. Following esophagectomy, ceiling effects were observed with self-care (86%), mobility (67%), anxiety/depression (55%) and pain/discomfort (19%) dimensions on EQ-5D-3L. For QLU-C10D ceiling effects were observed with emotional function (53%), physical function (16%), nausea (35%), sleep (31%), bowel problems (21%) and pain (20%). A strong correlation (r = 0.71) was observed between EQ-5D-3L anxiety and QLU-C10D emotional function dimensions. Good agreement (3.7% observations outside the limits of agreement) was observed between the utility scores.

Conclusion

The QLU-C10D is comparable to the more widely applied generic EQ-5D-3L, however, QLU-C10D was more sensitive to short-term utility changes following esophagectomy. Cognisant of requirements by policy makers to apply generic utility measures in cost effectiveness studies, the disease-specific QLU-C10D should be used alongside the generic measures like EQ-5D-3L.

Trial registration: The trial was registered with the Australian New Zealand Clinical Trial Registry (ACTRN12611000178943) on the 15th of February 2011.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-021-01867-w.

Estimation of an EORTC QLU-C10 Value Set for Spain Using a Discrete Choice Experiment

BACKGROUND

The EORTC QLU-C10D is a preference-based measure derived from the EORTC QLQ-C30. For use in economic evaluations, country-specific value sets are needed. This study aimed to generate an EORTC QLU-C10 value set for Spain.

METHODS

A sample of the Spanish general population completed an online discrete choice experiment. An attribute-balanced incomplete block design was used to select 960 choice tasks, with a total of 1920 health states. Each participant was randomly assigned 16 choice sets without replacement. Data were modelled using generalized estimating equations and mixed logistic regressions.

RESULTS

A total of 1625 panel members were invited to participate, 1010 of whom were included in the study. Dimension decrements were generally monotonic with larger disutilities at increased severity levels. Dimensions associated with larger decrements were physical functioning and pain, while the dimension with the smallest decrement was sleep disturbances. The PITS state (i.e. worst attainable health) for the Spanish population is - 0.043.

CONCLUSIONS

This study generated the first Spanish value set for the QLU-C10D. This can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30.

The Functional Assessment of Cancer Therapy Eight Dimension (FACT-8D), a Multi-Attribute Utility Instrument Derived From the Cancer-Specific FACT-General (FACT-G) Quality of Life Questionnaire: Development and Australian Value Set

OBJECTIVES

To develop a cancer-specific multi-attribute utility instrument derived from the Functional Assessment of Cancer Therapy - General (FACT-G) health-related quality of life (HRQL) questionnaire.

METHODS

We derived a descriptive system based on a subset of the 27-item FACT-G. Item selection was informed by psychometric analyses of existing FACT-G data (n = 6912) and by patient input (n = 82). We then conducted an online valuation survey, with participants recruited via an Australian general population online panel. A discrete choice experiment (DCE) was used, with attributes being the HRQL dimensions of the descriptive system and survival duration, and 16 choice-pairs per participant. Utility decrements were estimated with conditional logit and mixed logit modeling.

RESULTS

Eight HRQL dimensions were included in the descriptive system: pain, fatigue, nausea, sleep, work, social support, sadness, and future health worry; each with 5 levels. Of 1737 panel members who accessed the valuation survey, 1644 (95%) completed 1 or more DCE choice-pairs and were included in analyses. Utility decrements were generally monotonic; within each dimension, poorer HRQL levels generally had larger utility decrements. The largest utility decrements were for the highest levels of pain (-0.40) and nausea (-0.28). The worst health state had a utility of -0.54, considerably worse than dead.

CONCLUSIONS

A descriptive system and preference-based scoring approach were developed for the FACT-8D, a new cancer-specific multi-attribute utility instrument derived from the FACT-G. The Australian value set is the first of a series of country-specific value sets planned that can facilitate cost-utility analyses based on items from the FACT-G and related FACIT questionnaires containing FACT-G items.

United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

BACKGROUND

The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D.

METHODS

A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy.

RESULTS

A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead).

CONCLUSIONS

This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas

OBJECTIVE

The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas.

METHODS

An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents' demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R²) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models.

RESULTS

Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ-5D-5L index score was 0.81 (SD 0.21, range - 0.81-1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R², the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin's lymphoma sample; for Hodgkin's lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models.

CONCLUSION

The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas.

Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: Can disease-specific utilities be obtained?

Introduction

Innovations in head and neck cancer (HNC) treatment are often subject to economic evaluation prior to their reimbursement and subsequent access for patients. Mapping functions facilitate economic evaluation of new treatments when the required utility data is absent, but quality of life data is available. The objective of this study is to develop a mapping function translating the EORTC QLQ-C30 to EQ-5D-derived utilities for HNC through regression modeling, and to explore the added value of disease-specific EORTC QLQ-H&N35 scales to the model.

Methods

Data was obtained on patients with primary HNC treated with curative intent derived from two hospitals. Model development was conducted in two phases: 1. Predictor selection based on theory- and data-driven methods, resulting in three sets of potential predictors from the quality of life questionnaires; 2. Selection of the best out of four methods: ordinary-least squares, mixed-effects linear, Cox and beta regression, using the first set of predictors from EORTC QLQ-C30 scales with most correspondence to EQ-5D dimensions. Using a stepwise approach, we assessed added values of predictors in the other two sets. Model fit was assessed using Akaike and Bayesian Information Criterion (AIC and BIC) and model performance was evaluated by MAE, RMSE and limits of agreement (LOA).

Results

The beta regression model showed best model fit, with global health status, physical-, role- and emotional functioning and pain scales as predictors. Adding HNC-specific scales did not improve the model. Model performance was reasonable; R² = 0.39, MAE = 0.0949, RMSE = 0.1209, 95% LOA of -0.243 to 0.231 (bias -0.01), with an error correlation of 0.32. The estimated shrinkage factor was 0.90.

Conclusions

Selected scales from the EORTC QLQ-C30 can be used to estimate utilities for HNC using beta regression. Including EORTC QLQ-H&N35 scales does not improve the mapping function. The mapping model may serve as a tool to enable cost-effectiveness analyses of innovative HNC treatments, for example for reimbursement issues. Further research should assess the robustness and generalizability of the function by validating the model in an external cohort of HNC patients.

Converting EORTC QLQ-C30 scores to utility scores in the brigatinib ALTA study

Aims: Health utilities summarize a patient's overall health status. This study estimated utilities based on the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (QLQ-C30), a widely used measure of health-related quality-of-life (HRQoL) in oncology, using published mapping algorithms. Materials and methods: Data were from the Anaplastic Lymphoma Kinase (ALK) in Lung Cancer Trial of brigatinib (ALTA; NCT02094573), an open-label, international, phase 2 study. ALTA evaluated the efficacy and safety of two randomized dosing regimens of brigatinib in patients with locally advanced or metastatic ALK + non-small cell lung cancer (NSCLC) that had progressed on prior therapy with crizotinib. QLQ-C30 scores were mapped to European Quality-of-Life-5 Dimensions (EQ-5D) utility scores using two published algorithms (Khan et al. for EQ-5D-5L; Longworth et al. for EQ-5D-3L). The impact of brigatinib treatment on health utilities over time was assessed. Results: The analysis included 208 subjects. Mean baseline utility scores for both algorithms ranged between 0.60 - 0.71 and increased to 0.78 by cycle 5. Utility improvements were sustained during most of the treatment, before disease progression. Minor variations were observed between utility scores; Khan et al. estimates were approximately 0.01 or 0.02 points lower than Longworth et al. estimates. Limitations: Algorithms considered were limited to those available in the published literature at the time of the study. This utility analysis was exploratory, and the ALTA trial did not include an internal control group (i.e. standard of care) and was not powered to detect differences in QoL/utility outcomes between treatment arms. Conclusions: Converting QLQ-C30 scores into utilities in trials using established mapping algorithms can improve evaluation of medicines from the patient perspective. Both algorithms suggested that brigatinib improved health utility in crizotinib-refractory ALK + NSCLC patients, and improvements were maintained during most of the treatment. Clinicaltrials.gov identifier: NCT02094573.

The EORTC QLU-C10D: The Canadian Valuation Study and Algorithm to Derive Cancer-Specific Utilities From the EORTC QLQ-C30

Objective. The EORTC QLQ-C30 is widely used for assessing quality of life in cancer. However, QLQ-C30 responses cannot be incorporated in cost-utility analysis because they are not based on general population's preferences, or utilities. To overcome this limitation, the QLU-C10D, a cancer-specific utility algorithm, was derived from the QLQ-C30. The aim of this study was to obtain Canadian population utility weights for the QLU-C10D. Methods. Respondents from a Canadian research panel expressed their preferences for 16 choice sets in an online discrete choice experiment. Each choice set consisted of two health states described by the 10 QLU-C10D domains plus an attribute representing duration of survival. Using a conditional logit model, responses were converted into utility decrements by evaluating the marginal rate of substitution between each QLU-C10D domain level with respect to duration. Results. A total of 3,363 individuals were recruited. A total of 2,345 completed at least one choice set and 2,271 completed all choice sets. The largest utility decrements were associated with the worse levels of Physical Functioning (-0.24), Pain (-0.18), Role Functioning (-0.15), Emotional Functioning (-0.12), and Nausea (-0.12). The remaining domains and levels had decrements of -0.05 to -0.09. The utility of the worst possible health state was -0.15. Conclusion. Respondents from the general population were most concerned with generic health domains, but Nausea and Bowel Problems also had an impact on the individual's utility. It is unclear as to whether cancer-specific domains will affect cost-utility analysis when evaluating cancer treatments; this will be tested in the next phase of the study.

Mapping the cancer-specific QLQ-C30 onto the generic EQ-5D-5L and SF-6D in colorectal cancer patients

INTRODUCTION

Economic evaluation of healthcare interventions usually needs accurate data on utility and health-related quality-of-life scores. The aim of this study is to map QLQ-C30 scale score onto EQ-5D-5L and SF-6D utility values in colorectal cancer (CRC) patients.

METHODS

EQ-5D-5L, SF-6D, and QLQ-C30 were completed by 252 patients with CRC who were referred to three cancer centers in Tehran between May and September 2017. Moreover, OLS, Tobit, and CLAD models were used to predict EQ-5D-5L and SF-6D values. The goodness of fit of models was evaluated using Pred R² and Adj R². In addition, their predictive performance was assessed by MAE, RMSE, ICC, MID, and Spearman's correlation coefficients between observed and predicted EQ-5D-5L and SF-6D values. Models were validated using a 10-fold cross-validation method.

RESULTS

Considering the goodness of fit and predictive ability of models, the OLS Model 2 performed best for EQ-5D-5L (Adj R² = 58.09%, Pred R² = 58.93%, MAE = 0.0932, RMSE = 0.129) and the OLS Model 3 performed best for SF-6D (Adj R² = 54.90%, Pred R² = 55.62%, MAE = 0.0485, RMSE = 0.0634).

CONCLUSION

Our results demonstrated that algorithms developed based on OLS Models 1 and 2 are the best for predicted EQ-5D-5L and SF-6D values, respectively.

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

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.

Assessing Health-Related Quality of Life of Chinese Adults in Heilongjiang Using EQ-5D-3L

This study aimed to assess health-related quality of life (HRQOL) of Heilongjiang adult populations by using the EuroQol five-dimension three-level (EQ-5D-3L) questionnaire and to identify factors associated with HRQOL. Data from the National Health Services Survey (NHSS) 2008 in Heilongjiang province were obtained. Results of EQ-5D-3L questionnaires completed by 11,523 adult respondents (18 years or older) were converted to health index scores using a recently developed Chinese value set. Multivariate linear regression and logistic regression models were established to determine demographic, socioeconomic, health, and lifestyle factors that were associated with HRQOL and reported problems in the five dimensions of EQ-5D-3L. The Heilongjiang population had a mean EQ-5D-3L index score of 0.959. Lower EQ-5D-3L index scores were associated with older age, lower levels of education, chronic conditions, temporary accommodation, poverty, unemployment, and lack of regular physical activities. Older respondents and those who were unemployed, had chronic conditions, and lived in poverty were more likely to report problems in all of the five health dimensions. Higher educational attainment was associated with lower odds of reporting health problems in mobility, pain/discomfort, and anxiety/depression. Low socioeconomic status is associated with poor HRQOL. Regional population norms for EQ-5D-3L are needed for health economic studies due to great socioeconomic disparities across regions in China. Overall, the Heilongjiang population has a similar level of HRQOL compared with the national average.

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

PURPOSE

To derive a health state classification system (HSCS) from the cancer-specific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multi-attribute utility instrument.

METHODS

The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori selection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75 % observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions.

RESULTS

CFA supported the conceptual model and its generalisability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems.

CONCLUSIONS

The HSCS created from QLQ-C30 items is known as the EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The next phase of the QLU-C10D's development involves valuation studies, currently planned or being conducted across the globe.

Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report

Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. In 2005, ISPOR published the Good Research Practices for Cost-Effectiveness Analysis Alongside Clinical Trials: The ISPOR RCT-CEA Task Force report. ISPOR initiated an update of the report in 2014 to include the methodological developments over the last 9 years. This report provides updated recommendations reflecting advances in several areas related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members note that trials should be designed to evaluate effectiveness (rather than efficacy) when possible, should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. An incremental analysis should be conducted with an intention-to-treat approach, complemented by relevant subgroup analyses. Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.

Health related quality of life in patients with bladder cancer: a cross-sectional survey and validation study of the Hungarian version of the Bladder Cancer Index

Health-related quality of life (HRQoL) is an important outcome in oncology care although an underexplored area in bladder cancer (BC). Our aims were to assess HRQoL of patients with BC, analyse relationships between diverse HRQoL measures and validate the Hungarian version of the Bladder Cancer Index (BCI) questionnaire. A cross-sectional survey was performed among patients with BC (N = 151). Validated Hungarian versions of the FACT-Bl, SF-36 and EQ-5D were applied and SF-6D was derived. Psychometric analysis of the Hungarian BCI was performed. Pearson correlations between the five measures were analysed. Deterioration in SF-36 Physical Functioning was detected among patients aged 45-64 years. The EQ-5D score did not differ significantly from the age-matched population norm. Correlations between the FACT-Bl, EQ-5D and SF-6D utility measures were strong (r > 0.6). Cronbach alpha coefficients of the Hungarian BCI ranged from 0.75 to 0.97 and factor analysis confirmed that data fit to the six predefined subdomains. Test-retest correlations (reliability, N = 50) ranged from 0.67 to 0.87 and interscale correlations between urinary, bowel and sexual BCI domains were weak or moderate (r = 0.29 to 0.49). Convergent validity revealed a stronger correlation with FACT-Bl (r = 0.126 to 0.719) than with generic health state scores (r = 0.096 to 0.584). Results of divergent validity of the Hungarian BCI by treatment groups by Kruskal Wallis test were promising although limited by low sample sizes in cystectomy subgroups. Generic health state measures have limited capacity to capture HRQoL impact of BC. Validity tests yielded favourable results for the Hungarian BCI. Mapping studies to estimate utility scores from FACT-Bl are encouraged but less recommendable with the BCI.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach

Background

Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure.

Aim

To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure.

Methods

QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis.

Results

CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA.

Conclusion

CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Mapping FACT-P to EQ-5D in a large cross-sectional study of metastatic castration-resistant prostate cancer patients

Purpose

To construct a model to predict preference-adjusted EuroQol 5D (EQ-5D) health utilities for patients with metastatic castrate-resistant prostate cancer (mCRPC) using the disease-specific health-related quality of life (HRQoL) measure, functional assessment of cancer therapy-prostate (FACT-P).

Methods

HRQoL data were collected from patients with mCRPC who were enrolled in an observational study conducted in 47 centers across six European Union countries. Utility values were generated using a UK-specific EQ-5D value set. The predictive validity of the five FACT-P subscales, patient demographics, comorbidities and prior chemotherapy was tested using ordinary least squares (OLS), median, Gamma and Tobit multivariate regression models.

Results

FACT-P and EQ-5D questionnaires were completed by 602 (86 %) patients. Mean age [standard deviation (SD)] was 72.1 (7.9) years, mean time from diagnosis (SD) was 5.4 (4.4) years, and mean time since failure of androgen deprivation therapy (SD) was 1.0 (1.6) years. At study inclusion, 39 % of patients were chemotherapy-naïve, 37 % were undergoing chemotherapy, and 24 % were post-chemotherapy. Mean FACT-P and EQ-5D utility values were 104 and 0.66, respectively. OLS regression was the best-performing model, explaining 61.2 % of the observed EQ-5D variation. All FACT-P subscales were significantly predictive; the physical and functional well-being subscales had the highest explanatory value (coefficient 0.023 and 0.001, respectively, p < 0.0001). The other variables did not add additional explanatory value.

Conclusions

The algorithm developed enables translation of cancer-specific HRQoL measures to preference-adjusted health status in patients with mCRPC. The function may be useful in calculating EQ-5D scores when EQ-5D data have not been gathered directly.

Mapping algorithms from QLQ-C30 to EQ-5D utilities: no firm ground to stand on yet

AIM

Over the last years several mapping or cross-walking algorithms for deriving utilities from QLQ-C30 scores have been published. However their external predictive accuracy has not yet been systematically compared.

METHODS

We tested the external validity of previously published mapping algorithms to transform the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire responses to EQ-5D derived Utilities.

RESULTS

When applied to different data sets, the currently published mapping showed a large variation between algorithms of the values of the mapped utilities, a low accuracy of the mapping compared to the observed EQ-5D utilities and no consistent performance between competing algorithms.

DISCUSSION

Therefore direct mapping from QLQ-C30 profiles to EQ-5D utilities using published algorithms should be viewed cautiously.

Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D

Objective

To help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures.

Methods

Cancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and SF-6D. Responses were randomly divided to form development (n = 184) and cross-validation (n = 183) samples. Relationships between the instruments were estimated using ordinary least squares (OLS), generalized linear models (GLM), and censored least absolute deviations (CLAD) regression approaches. The performance of each model was assessed in terms of how well the responses to the cancer-specific instrument predicted EQ-5D and SF-6D utilities using mean absolute error (MAE) and root mean squared error (RMSE).

Results

Physical, functional, and emotional well-being domain scores of the FACT-G best explained the EQ-5D and SF-6D. In terms of accuracy of prediction as measured in RMSE, the CLAD model performed best for the EQ-5D (RMSE = 0.095) whereas the GLM model performed best for the SF-6D (RMSE = 0.061). The GLM predicted SF-6D scores matched the observed values more closely than the CLAD and OLS.

Conclusion

Our results demonstrate that the estimation of both EQ-5D and SF-6D utility indices using the FACT-G responses can be achieved. The CLAD model for the EQ-5D and the GLM model for the SF-6D are recommended. Thus, it is possible to estimate quality-adjusted life years for economic evaluation from studies where only cancer-specific instrument have been administered.