Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma

Mapping the Oxford Shoulder Score onto the EQ-5D utility index

PURPOSE

In order to enable cost-utility analysis of shoulder pain conditions and treatments, this study aimed to develop and evaluate mapping algorithms to estimate the EQ-5D health index from the Oxford Shoulder Score (OSS) when health outcomes are only assessed with the OSS.

METHODS

5437 paired OSS and EQ-5D questionnaire responses from four national multicentre randomised controlled trials investigating different shoulder pathologies and treatments were split into training and testing samples. Separate EQ-5D-3L and EQ-5D-5L analyses were undertaken. Transfer to utility (TTU) regression (univariate linear, polynomial, spline, multivariable linear, two-part logistic-linear, tobit and adjusted limited dependent variable mixture models) and response mapping (ordered logistic regression and seemingly unrelated regression (SUR)) models were developed on the training sample. These were internally validated, and their performance evaluated on the testing sample. Model performance was evaluated over 100-fold repeated training-testing sample splits.

RESULTS

For the EQ-5D-3L analysis, the multivariable linear and splines models had the lowest mean square error (MSE) of 0.0415. The SUR model had the lowest mean absolute error (MAE) of 0.136. Model performance was greatest in the mid-range and best health states, and lowest in poor health states. For the EQ-5D-5L analyses, the multivariable linear and splines models had the lowest MSE (0.0241-0.0278) while the SUR models had the lowest MAE (0.105-0.113).

CONCLUSION

The developed models now allow accurate estimation of the EQ-5D health index when only the OSS responses are available as a measure of patient-reported health outcome.

Feasibility of Deriving Health State Utilities in Mycosis Fungoides Cutaneous T-Cell Lymphoma Using Mapping Algorithms

BACKGROUND

A prospective, observational, US-based study (PROVe) used three questionnaires (Pruritus-VAS, Skindex-29, MF/SS-CTCL QoL) to assess quality of life in patients diagnosed with mycosis fungoides cutaneous T-cell lymphoma (MF-CTCL); however, none of these studies was provided with a preference-based algorithm yielding health state utility values (HSUVs).

OBJECTIVE

This study aimed to assess the feasibility of deriving HSUVs from published mapping algorithms by comparing mapped utilities with the HSUVs reported in the MF-CTCL literature.

METHODS

We searched PubMed, the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0) to identify any studies mapping Pruritus-VAS, Skindex-29, or MF/SS-CTCL QoL to a preference-based instrument (ideally, EQ-5D), and any studies assessing HSUVs in MF-CTCL. Two algorithms from a recent study that mapped Pruritus-VAS onto EQ-5D-3L were applied to the PROVe patient-level data. We performed multiple imputation to handle missing VAS data, calculated average mapped utilities in the whole sample, and compared them with relevant factors using the t-test and one-way analysis of variance (ANOVA).

RESULTS

Overall, 298 patients provided 1441 Pruritus-VAS scores over a 2-year follow-up (1-21 visits per patient). The average mapped HSUVs ranged between 0.950 and 0.999 depending on the algorithm applied and imputation of missing data. In subgroup analysis, significant differences (p < 0.05) were observed according to age, race, and cancer stage. A few previous studies that collected HSUVs from MF-CTCL patients reported mean values of between 0.82 and 0.87 using time trade-off, 0.63 and 0.83 using EQ-5D, and 0.51 and 0.69 using the HUI3.

CONCLUSIONS

The HSUVs derived by applying published mapping algorithms to PROVe Pruritus-VAS data appeared largely overestimated if compared with the existing literature. More research is required to understand the applicability of existing mapping algorithms and to develop new mapping algorithms in MF-CTCL.

Bridging health technology assessment and healthcare quality improvement using international consortium of health outcomes measurement standard sets

Objective

Although health technology assessment (HTA) and healthcare quality improvement are distinct processes, a greater level of alignment in outcome measures used may increase the quality and efficiency of data collection. This study evaluates the agreement in outcome measures used in oncology for healthcare quality improvement and HTAs, and how these align to the International Consortium for Health Outcomes Measurement (ICHOM) standard sets.

Methods

We conducted a cross-sectional comparative analysis of ICHOM sets focusing on oncological indications and publicly available measures for healthcare quality and HTA reports published by the National Health Care Institute from the Netherlands and the National Institute for Health and Care Excellence from the United Kingdom.

Results

All ICHOM sets and HTAs used overall survival, whereas quality improvement used different survival estimates. Different progression estimates for cancer were used in HTAs, ICHOM sets, and quality improvement. Data on health-related quality of life (HRQoL) was recommended in all ICHOM sets and all HTAs, but selectively for quality improvement. In HTAs, generic HRQoL questionnaires were preferred, whereas, in quality improvement and ICHOM sets, disease-specific questionnaires were recommended. Unfavorable outcomes were included in all HTAs and all ICHOM sets, but not always for quality improvement.

Conclusions

Although HTA and quality improvement use outcome measures from the same domains, a greater level of alignment seems possible. ICHOM may provide input on standardized outcome measures to support this alignment. However, residual discrepancies will remain due to the different objectives of HTA and quality improvement.

'Mapping' Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases

BACKGROUND

The use of patient-reported outcome measures (PROMs) to monitor the effects of disease and treatment on patient symptomatology and daily life is increasing in rare diseases (RDs) (i.e. those affecting less than one in 2000 people); however, these instruments seldom yield health state utility values (HSUVs) for cost-utility analyses. In such a context, 'mapping' allows HSUVs to be obtained by establishing a statistical relationship between a 'source' (e.g. a disease-specific PROM) and a 'target' preference-based measure [e.g. the EuroQol-5 Dimension (EQ-5D) tool].

OBJECTIVE

This study aimed to systematically review all published studies using 'mapping' to derive HSUVs from non-preference-based measures in RDs, and identify any critical issues related to the main features of RDs, which are characterised by small, heterogeneous, and geographically dispersed patient populations.

METHODS

The following databases were searched during the first half of 2019 without time, study design, or language restrictions: MEDLINE (via PubMed), the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0). The keywords combined terms related to 'mapping' with Orphanet's list of RD indications (e.g. 'acromegaly') in addition to 'rare' and 'orphan'. 'Very rare' diseases (i.e. those with fewer than 1000 cases or families documented in the medical literature) were excluded from the searches. A predefined, pilot-tested extraction template (in Excel^®) was used to collect structured information from the studies.

RESULTS

Two groups of studies were identified in the review. The first group (n = 19) developed novel mapping algorithms in 13 different RDs. As a target measure, the majority used EQ-5D, and the others used the Short-Form Six-Dimension (SF-6D) and 15D; most studies adopted ordinary least squares (OLS) regression. The second group of studies (n = 9) applied previously published algorithms in non-RDs to comparable RDs, mainly in the field of cancer. The critical issues relating to 'mapping' in RDs included the availability of very few studies, the relatively high number of cancer studies, and the absence of research in paediatric RDs. Moreover, the reviewed studies recruited small samples, showed a limited overlap between RD-specific and generic PROMs, and highlighted the presence of cultural and linguistic factors influencing results in multi-country studies. Lastly, the application of existing algorithms developed in non-RDs tended to produce inaccuracies at the bottom of the EQ-5D scale, due to the greater severity of RDs.

CONCLUSIONS

More research is encouraged to develop algorithms for a broader spectrum of RDs (including those affecting young children), improve mapping study quality, test the generalisability of algorithms developed in non-RDs (e.g. HIV) to rare variants or evolutions of the same condition (e.g. AIDS wasting syndrome), and verify the robustness of results when mapped HSUVs are used in cost-utility models.

Health-related quality of life around the time of diagnosis in patients with bladder cancer

OBJECTIVES

To quantify the health-related quality of life (HRQoL) of patients with bladder cancer around the time of diagnosis and to test the hypotheses of a two-factor model for the HRQoL questionnaire QLQ-C30.

METHODS

From participants in the Bladder Cancer Prognoses Programme, a multicentre cohort study, sociodemographic data were collected using semi-structured face-to-face interviews. Answers to the QLQ-C30 were transformed into a scale from 0 to 100. HRQoL data were analysed in multivariate analyses. The hypothesized two-factor (Physical and Mental Health) domain structure of the QLQ-C30 was also tested with confirmatory factor analyses (CFA).

RESULTS

A total of 1160 participants (78%) completed the questionnaire after initial visual diagnosis and before pathological confirmation. Despite non-muscle-invasive bladder cancer (NMIBC) being associated with a higher HRQoL than carcinoma invading bladder muscle, only the domain Role Functioning was clinically significantly better in patients with NMIBC. Age, gender, bladder cancer stage and comorbidity all had a significant influence on QLQ-C30 scores. The CFA showed an overall good fit of the hypothesized two-factor model.

CONCLUSION

This study identified a baseline reference value for HRQoL for patients with bladder cancer, which allows better evaluation of any changes in HRQoL as disease progresses or after treatment. In addition, a two-factor (Physical and Mental Health) model was developed for the QLQ-C30.

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.

Mapping health-related quality of life scores from FACT-G, FAACT, and FACIT-F onto preference-based EQ-5D-5L utilities in non-small cell lung cancer cachexia

BACKGROUND

Health-related quality of life (HRQoL) measurements from disease-specific tools cannot be directly used in economic evaluations. This study aimed to develop and validate mapping algorithms that predicted EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) utilities from Functional Assessment of Anorexia-Cachexia Therapy (FAACT) and Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) and their common component (Functional Assessment of Cancer Therapy-General-FACT-G) in patients with non-small cell lung cancer cachexia.

METHODS

Data were collected on five occasions over a 12-week period in two multicenter placebo-controlled trials. EQ-5D-5L utilities were calculated using both English and Dutch value sets. The study sample was divided into development and validation datasets according to patients' geographical residence. Generalized estimating equations were applied to five different sets of independent variables including overall, Trial Outcome Index (TOI), and individual subscales results. The best performing models were selected based on mean absolute error (MAE) and root-mean square error (RMSE).

RESULTS

EQ-5D-5L and FAACT/FACIT-F results were available for 96 patients. The developed algorithms showed a good predictive performance, with acceptable MAE/RMSE and small differences between mean observed and predicted EQ-5D-5L utilities. In FACT-G models, Physical Well-Being had the highest explanatory value, while Emotional Well-Being did not significantly affect the EQ-5D-5L score; Anorexia-Cachexia and Fatigue subscales were highly statistically significant in FAACT and FACIT-F models, respectively, as well as the TOI scores. The Eastern Cooperative Oncology Group status was included as covariate in all models.

CONCLUSION

The developed algorithms enable the estimation of EQ-5D-5L utilities from three cancer-specific instruments when preference-based HRQoL data are missing.

The use of patient-reported outcome measures (PROMs) in the management of malignant pleural mesothelioma: a descriptive literature survey

Background

Malignant pleural mesothelioma (MPM) remains a highly symptomatic and aggressive malignancy. The treatment options are for most patients limited to palliative chemotherapy and best supportive care. Therefore, the use of patient-reported outcome measures (PROMs) is recommended for the improvement of the quality of care. The aim of this literature survey is to provide an up to date review of the use of PROMs in mesothelioma. A concise comparison is made of the identified instruments.

Methods

We searched PubMed, Web of Science and Google Scholar for the use of PROMs in MPM. Quality of the study and risk of bias were assessed using the appraisal tools recommended by the Dutch Cochrane Center.

Results

A total of 31 articles on PROMs in MPM were identified that met the inclusion criteria and a total of 14 instruments. The instruments are categorized in generic (n=2), cancer-specific (n=4), lung cancer-specific (n=3), mesothelioma-specific (n=2) and symptom-specific (n=3). They were mostly used in clinical trials.

Conclusions

PROMs have the potential to improve the management of MPM. No particular instrument is specifically recommended, although there is a preference for patient-reported disease-specific instruments encompassing the concept of health-related quality of life (hrQoL) and relevant symptoms. Such instruments are the EORTC QLQ-LC13, LCSS-Meso and FACT-L, which measure the impact of malignant mesothelioma and its treatment on patients. Assessments should be made on baseline and post-treatment. The frequency of assessments should be further evaluated in this population.

Mapping the EORTC-QLQ-C30 to the EQ-5D-3L: An Assessment of Existing and Newly Developed Algorithms

Objectives. To assess the external validity of mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses not previously validated and to assess whether statistical models not previously applied are better suited for mapping the EORTC QLQ-C30 to the EQ-5D-3L. Methods. In total, 3866 observations for 1719 patients from a longitudinal study (Cancer 2015) were used to validate existing algorithms. Predictive accuracy was compared to previously validated algorithms using root mean squared error, mean absolute error across the EQ-5D-3L range, and for 10 tumor-type specific samples as well as using differences between estimated quality-adjusted life years. Thirteen new algorithms were estimated using a subset of the Cancer 2015 data (3203 observations for 1419 patients) applying various linear, response mapping, beta, and mixture models. Validation was performed using 2 data sets composed of patients with varying disease severity not used in the estimation and all available algorithms ranked on their performance. Results. None of the 5 existing algorithms offer an improvement in predictive accuracy over preferred algorithms from previous validation studies. Of the newly estimated algorithms, a 2-part beta model performed the best across the validation criteria and in data sets composed of patients with different levels of disease severity. Validation results did, however, vary widely between the 2 data sets, and the most accurate algorithm appears to depend on health state severity as the distribution of observed EQ-5D-3L values varies. Linear models performed better for patients in relatively good health, whereas beta, mixture, and response mapping models performed better for patients in worse health. Conclusion. The most appropriate mapping algorithm to apply in practice may depend on the disease severity of the patient sample whose utility values are being predicted.

Improved cost-effectiveness of short-course radiotherapy in elderly and/or frail patients with glioblastoma

BACKGROUND AND PURPOSE

Short-course radiotherapy (25 Gy in five fractions) was recently shown in a randomized phase III trial to be non-inferior to 40 Gy in 15 fractions in elderly and/or frail patients with glioblastoma multiforme. This study compared the cost-effectiveness of the two regimens.

MATERIAL AND METHODS

The direct unit costs of imaging, radiotherapy (RT), and dexamethasone were collected from the five primary contributing countries to the trial, constituting the data of 88% of all patients. Effectiveness was measured by the restricted mean overall survival (RMOS) and progression free survival (RMPFS). The incremental cost-effectiveness ratio (ICER) was calculated. Indirect costs were also estimated for comparison.

RESULTS

The median OSs for the short-course and commonly used RTs were 8.2 (95% confidence interval [CI] 6.1-10.3) and 7.7 (95% CI 5.5-9.9) months, respectively (log rank p = 0.340). Median PFSs were also not different (p = 0.686). The differences in the RMOS and the ICER, however, were +0.11 life-years and -$3062 United States dollars (USD) per life-year gained, respectively. The differences in the RMPFS and the ICER were +0.02 PFS and -$17,693 USD, respectively.

CONCLUSION

The ICER of -$3062 per life-year gained and -$17,693 per PFS gained indicates that the short-course RT is less costly compared to the longer RT regimen.

The External Validity of Mapping MSIS-29 on EQ-5D Among Individuals With Multiple Sclerosis in Sweden

Background: Mapping can be performed to predict utility values from condition-specific measures when preference-based measures are absent. A previously developed algorithm that predicts EQ-5D-3L index values from the Multiple Sclerosis Impact Scale (MSIS-29) has not yet been externally validated. Aim: To examine the external validity of a previously developed mapping algorithm by testing the accuracy of predicting EQ-5D-3L index values from MSIS-29 among multiple sclerosis (MS) patients in Sweden. Methods: Cross-sectional individual-level data were collected from population-based Swedish registers between 2011 and 2014. Health-related quality of life was assessed through MSIS-29 and EQ-5D-3L at one point in time among 767 individuals with known disability level of MS. A previously developed mapping algorithm was applied to predict EQ-5D index values from MSIS-29 items, and the predictive accuracy was assessed through mean absolute error and root mean square error. Results: When applying the algorithm, the predicted mean EQ-5D-3L index value was 0.77 compared to the observed mean index value of 0.75. Prediction error was higher for individuals reporting EQ-5D values <0.5 compared to individuals reporting EQ-5D values ≥0.5. Mean absolute error (0.12) and root mean square error (0.18) were smaller or equal to the prediction errors found in the original mapping study. Conclusion: The mapping algorithm had similar predictive accuracy in the two independent samples although results showed that the highest predictive performance was found in groups with better health. Varied predictive accuracy in subgroups is consistent with previous studies and strategies to deal with this are warranted.

Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients

Background

Several mapping algorithms have been published with the EORTC-QLQ-C30 for estimating EQ-5D-3L utilities. However, none are available with EQ-5D-5L. Moreover, a comparison between mapping algorithms in the same set of patients has not been performed for these two instruments simultaneously. In this prospective data set of 100 non-small cell lung cancer (NSCLC) patients, we investigate three mapping algorithms using the EQ-5D-3L and EQ-5D-5L and compare their performance.

Methods

A prospective non-interventional cohort of 100 NSCLC patients were followed up for 12 months. EQ-5D-3L, EQ-5D-5L and EORTC-QLQ-C30 were assessed monthly. EQ-5D-5L was completed at least 1 week after EQ-5D-3L. A random effects linear regression model, a beta-binomial (BB) and a Limited Variable Dependent Mixture (LVDM) model were used to determine a mapping algorithm between EQ-5D-3L, EQ-5D-5L and QLQ-C30. Simulation and cross validation and other statistical measures were used to compare the performances of the algorithms.

Results

Mapping from the EQ-5D-5L was better: lower AIC, RMSE, MAE and higher R² were reported with the EQ-5D-5L than with EQ-5D-3L regardless of the functional form of the algorithm. The BB model proved to be more useful for both instruments: for the EQ-5D-5L, AIC was –485, R² of 75 %, MAE of 0.075 and RMSE was 0.092. This was –385, 69 %, 0.099 and 0.113 for EQ-5D-3L respectively. The mean observed vs. predicted utilities were 0.572 vs. 0.577 and 0.515 vs. 0.523 for EQ-5D-5L and EQ-5D-3L respectively, for OLS; for BB, these were 0.572 vs. 0.575 and 0.515 vs. 0.518 respectively and for LVDMM 0.532 vs 0.515 and 0.569 vs 0.572 respectively. Less over-prediction at poorer health states was observed with EQ-5D-5L.

Conclusions

The BB mapping algorithm is confirmed to offer a better fit for both EQ-5D-3L and EQ-5D-5L. The results confirm previous and more recent results on the use of BB type modelling approaches for mapping. It is recommended that in studies where EQ-5D utilities have not been collected, an EQ-5D-5L mapping algorithm is used.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms

PURPOSE

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer.

METHODS

A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness.

RESULTS

Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity.

CONCLUSIONS

Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

Mapping Functions in Health-Related Quality of Life: Mapping from Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L

Background. Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. Methods. Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. Results. For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. Conclusions. OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health.