Mapping IWQOL-Lite onto EQ-5D-5L and SF-6Dv2 among overweight and obese population in China

PURPOSE

To develop the mapping functions from the Impact of Weight on Quality of Life-Lite (IWQOL-Lite) scores onto the EQ-5D-5L and SF-6Dv2 utility values among the overweight and obese population in China.

METHODS

A representative sample of the overweight and obese population in China stratified by age, sex, body mass index (BMI), and area of residence was collected by online survey and the sample was randomly divided into development (80%) and validation (20%) datasets. The conceptual overlap between the IWQOL-Lite and the EQ-5D-5L or SF-6Dv2 was evaluated by Spearman's correlation coefficients. Five models, including OLS, Tobit, CLAD, GLM, and PTM were explored to derive mapping functions using the development dataset. The model performance was assessed using MAE, RMSE, and the percentage of AE > 0.05 and AE > 0.1 in the validation dataset.

RESULTS

A total of 1000 respondents (48% female; mean [SD] age: 51.7 [15.3]; mean [SD] BMI: 27.4 [2.8]) were included in this study. The mean IWQOL-Lite scores and the utility values of EQ-5D-5L and SF-6Dv2 were 78.5, 0.851, and 0.734, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities both used IWQOL-Lite total score as a predictor in the CLAD model (MAE: 0.083 and 0.076 for the EQ-5D-5L and SF-6Dv2; RMSE: 0.125 and 0.103 for the EQ-5D-5L and SF-6Dv2; AE > 0.05: 20.5% and 27.5% for the EQ-5D-5L and SF-6Dv2; AE > 0.10: 9.5% and 15.0% for the EQ-5D-5L and SF-6Dv2).

CONCLUSION

CLAD models with the IWQOL-Lite total score can be used to predict both the EQ-5D-5L and SF-6Dv2 utility values among overweight and obese population in China.

Cost-effectiveness of ravulizumab compared with eculizumab for the treatment of paroxysmal nocturnal hemoglobinuria in the Netherlands

OBJECTIVES

The aim of this study was to evaluate the cost-effectiveness of ravulizumab compared with eculizumab for the treatment of adult patients with paroxysmal nocturnal hemoglobinuria (PNH) in the Netherlands.

METHODS

A cost-effectiveness analysis was conducted based on a Markov cohort model simulating the course of patients with PNH with clinical symptom(s) indicative of high disease activity, or who are clinically stable after having been treated with eculizumab for at least the past six months. Costs, quality of life, and the incremental cost-effectiveness ratio (ICER) were estimated over a lifetime horizon from a Dutch societal perspective. Several additional analyses were performed, including a one-way sensitivity analysis, a probabilistic sensitivity analysis, and scenario analysis.

RESULTS

When compared with eculizumab, ravulizumab saves €266,833 and 1.57 quality adjusted life years (QALYs) are gained, resulting in a dominant ICER. Drug costs account for the majority of the total costs in both intervention groups. Cost savings were driven by the difference in total treatment costs of ravulizumab compared with eculizumab caused by the reduced administration frequency, accounting for 98% of the total cost savings. The QALY gain with ravulizumab is largely attributable to the improved quality of life associated with less frequent infusions and BTH events. At a willingness-to-pay threshold of €20,000/QALY, there is a 76.6% probability that ravulizumab would be cost-effective.

CONCLUSIONS

The cost reduction and QALY gain associated with the lower rates of BTH and less frequent administration make ravulizumab a cost-saving and clinically beneficial substitute for eculizumab for adults with PNH in the Netherlands.

Using a Sample Size Calculation Framework for Clinical Prediction Models When Developing and Selecting Mapping Algorithms Based on Linear Regression

PURPOSE

To propose using a framework for calculating the sample size for clinical prediction models when developing and selecting mapping algorithms from a health-related quality-of-life (HRQOL) measure onto the score of a preference-based measure (PBM) using linear regression.

METHODS

The framework was summarized for health economics researchers. Mapping studies that mapped the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the EQ-5D-3L index using linear regression were evaluated in terms of sample size. The required sample size for each study was calculated using 4 criteria: global shrinkage factor ≥ 0.9, difference between the apparent and adjusted R2 ≤ 0.05, multiplicative margin of error in the estimated residual standard deviation ≤ 1.1, and absolute margin of error in the estimated model intercept ≤ 0.025.

RESULTS

Ten mapping studies were identified. The information required to calculate the sample size was successfully extracted from previous mapping studies. Four of 10 mapping studies did not have sufficient sample sizes.

LIMITATIONS

Further extension of this framework to other regression approaches used in mapping studies is necessary.

CONCLUSIONS

The sample size should be considered when developing and selecting a mapping algorithm based on linear regression.

HIGHLIGHTS

No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous mapping studies and that 4 of 10 mapping studies did not have sufficient sample sizes.

Mapping Studies to Estimate Health-State Utilities From Nonpreference-Based Outcome Measures: A Systematic Review on How Repeated Measurements are Taken Into Account

OBJECTIVES

Mapping algorithms are developed using data sets containing patient responses to a preference-based questionnaire and another health-related quality-of-life questionnaire. When data sets include repeated measurements from the same individuals over time, the assumption of observations' independence, required by standard models, is violated, and standard errors are underestimated. This review aimed to identify how studies deal with methodological challenges of repeated measurements, provide an overview of practice to date, and potential implications for future work.

METHODS

We conducted a systematic literature search of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, specialized databases, and previous systematic reviews. A data template was used to extract, among others, start and target instruments if the data set(s) used for estimation and validation had repeated measurements per patient, used regression techniques, and which (if any) adjustments were made for repeated measurements.

RESULTS

We identified 278 publications developing at least 1 mapping algorithm. Of the 278 publications, 121 used a data set with repeated measurements, among which 92 used multiple time points for estimation, and 39 selected specific time points to have 1 observation per participant. A total of 36 studies did not account for repeated measurements. An adjustment was conducted using cluster-robust standard errors (21), random-effects models (30), generalized estimating equations (7), and other methods (7).

CONCLUSIONS

The inconsistent use of methods to account for interdependent observations in the literature indicates that mapping guidelines should include recommendations on how to deal with repeated measurements, and journals should update their guidelines accordingly.

Is ultra-hypo-fractionated radiotherapy more cost-effective relative to conventional fractionation in treatment of prostate cancer? A cost-utility analysis alongside a randomized HYPO-RT-PC trial

BACKGROUND

Economic evidence for comparing low fraction with ultra-hypo fractionated (UHF) radiation therapy in the treatment of intermediate-to-high-risk prostate cancer (PC) is lacking, especially in Europe. This study presents an economic evaluation performed alongside an ongoing clinical trial.

AIM

To investigate up to 6 years' follow-up whether conventional fractionation (CF, 78.0 Gy in 39 fractions, 5 days per week for 8 weeks) is more cost-effective than UHF (42.7 Gy in 7 fractions, 3 days per week for 2.5 weeks inclusive of 2 weekends) radiotherapy in treatment for patients with intermediate-to-high-risk PC.

METHOD

HYPO-RT-PC trial is an open-label, randomized, multicenter (10 in Sweden; 2 in Denmark) phase-3 trial. Patients from Sweden (CF 434; UHF 445) were included in this study. The trial database was linked to the National Patient Registry (NPR). Costs for inpatient/non-primary outpatient care for each episode were retrieved. For calculating Quality-adjusted life years (QALYs), the EORTC QLQ-C30 questionnaire was mapped to the EQ-5D-3L index. Multivariable regression analyses were used to compare the difference in costs and QALYs, adjusting for age and baseline costs, and health status. The confidence interval for the difference in costs, QALYs and incremental cost-effectiveness ratio effectiveness ratio (ICER) was estimated by the bootstrap percentile method.

RESULTS

No significant differences were found in ICER between the two arms after 6 years of follow-up.

CONCLUSION

The current study did not support that the ultra-hypo-fractionated treatment was more cost-effective than the conventional fraction treatment up to the sixth year of the trial.

Estimation of Health-Related Utilities for 177Lu-DOTATATE in GEP-NET Patients Using Utilities Mapped from EORTC QLQ-C30 to EQ-5D-3L and QLU-C10D Utilities

BACKGROUND

Gastroenteropancreatic neuroendocrine tumours (GEP-NET) are a rare, life-threatening type of cancer. The survival benefit of ¹⁷⁷Lu-DOTATATE has been demonstrated in GEP-NET patients. Health technology assessment bodies require data on health-related utility impacts of treatment. A cancer-specific instrument, EORTC QLQ-C30, was used to collect the data for ¹⁷⁷Lu-DOTATATE within clinical studies, but utility-based instruments were not included.

OBJECTIVE

The main aim of this study was to compare EQ-5D-3L and QLU-C10D utilities obtained from EORTC QLQ-C30 using two different approaches. A secondary aim was to analyse the EQ-5D-3L and QLU-C10D utilities of patients treated with ¹⁷⁷Lu-DOTATATE versus best supportive care. A supplementary aim was to evaluate the effect of ¹⁷⁷Lu-DOTATATE on patients' health-related utility over time.

METHODS

Three datasets were used for the analysis. NETTER-1 is a clinical trial, whilst ERASMUS and Guy's and St. Thomas (GStT) are real-world datasets. Two mapping algorithms (response mapping and ordinary least square regression) were applied to generate EQ-5D-3L utilities from EORTC QLQ-C30. An algorithm was used to obtain QLU-C10D utilities from EORTC QLQ-C30.

RESULTS

In all studies, EQ-5D-3L utilities were higher than QLU-C10D utilities at most time points measured, although the magnitude of the differences was small. In NETTER-1, EQ-5D-3L and QLU-C10D utilities were higher in the ¹⁷⁷Lu-DOTATATE arm compared with the octreotide long-acting release (LAR) arm, overall and pre-progression. In all studies, patients' health-related utilities seem to be maintained over time.

CONCLUSION

There were small differences between EQ-5D-3L and QLU-C10D utilities, but these did not translate to relative differences over time or between groups. In NETTER-1, patients in the ¹⁷⁷Lu-DOTATATE arm had higher health-related utilities than patients in the octreotide LAR arm. Health-related utility may at least remain maintained in patients with GEP-NET receiving ¹⁷⁷Lu-DOTATATE.

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 EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients

PURPOSE

Patient-level utility data are needed for cost-utility analysis; in oncology, however, the data are commonly gathered using disease-specific questionnaires that are often not appropriate. Present study aimed to derive an algorithm which can map the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) scales and the Colorectal Cancer-Specific Quality Of Life Questionnaire (QLQ-CR29) scales onto the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) values in patients with colorectal cancer (CRC).

METHODS

Using the Ordinary Least Square (OLS) model, a cross-sectional dataset of 252 patients with CRC were gathered from three academic centers of cancer treatment in Tehran in 2017. The predicted R² (Pred R²) and adjusted R² (Adj R²) are used to evaluate model goodness of fit. Additionally, mean absolute error (MAE), root mean square error (RMSE), Spearman's correlation coefficients (ρ), and intraclass correlation (ICC) are applied to assess predictive ability of models. The tenfold cross-validation procedure was applied for validation models.

RESULTS

According to the results of our study, the model C4 from EORTC QLQ-C30 was the best predictive model (Pred R² = 66.57%, Adj R² = 67.67%, RMSE = 0.10173, MAE = 0.07840). Also, the model R4 from QLQ-CR29 performed the best for EQ-5D-5L (Adj R2 = 48.42%, Pred R² = 45.54%, MAE = 0.10051, RMSE = 0.12997).

CONCLUSIONS

The mapping algorithm successfully mapped the EORTC QLQ-C30 and QLQ-CR29 scales onto the EQ-5D-5L values; therefore, it enables policymakers to convert cancer-specific questionnaires scores to the preference-based scores.

Evaluation of the performance of algorithms mapping EORTC QLQ-C30 onto the EQ-5D index in a metastatic colorectal cancer cost-effectiveness model

Background

Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated.

Methods

Three available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model.

Results

The available algorithms yielded mean utilities of 1: 0.87 ± sd:0.14,2: 0.81 ± 0.15 (both Dutch tariff) and 3: 0.81 ± sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83 ± 0.17 with Dutch tariff, 0.80 ± 0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models.

Conclusion

Available mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study.

Predictive ability of the American Society of Anaesthesiologists physical status classification system on health-related quality of life of patients after total hip replacement: comparisons across eight EQ-5D-3L value sets

Background

American Society of Anaesthesiologists (ASA) physical status classification system and its association with postoperative outcomes has been studied in different diseases. However, there is a paucity of studies on the relationship between ASA class and postoperative health-related quality of life (HRQoL) outcomes following total hip replacement (THR).

The aim of this study was to assess the discriminative abilities of EQ-5D-3L value sets from Sweden, Germany, Denmark and the United Kingdom in relation to ASA classes and these value sets’ abilities to show the predictive performance of ASA classes on HRQoL among THR patients in Sweden.

Methods

A longitudinal study was conducted using data of patients in the Swedish Hip Arthroplasty Register who underwent THR between 2008 and 2016. We included 69,290 pre- and 1-year postoperative records and 21,305 6-year postoperative records. The study examined three experience-based EQ-5D-3L value sets (the Swedish VAS and TTO and the German VAS) and five hypothetical value sets (TTO from Germany and VAS and TTO value sets from Denmark and the UK each). Using linear models, the abilities of the value sets to discriminate among ASA classes and to show the predictive performance of ASA classes on HRQoL score were assessed.

Results

All value sets differentiated among ASA classes and showed the predictive effect of ASA classes on HRQoL. ASA classes were found to predict HRQoL consistently for all value sets investigated, with small variations in prediction error among the models.

Conclusion

ASA classes of patients undergoing THR predicted HRQoL scores significantly and consistently, indicating their importance in tailoring care for patients.

EQ-5D-Derived Health State Utility Values in Hematologic Malignancies: A Catalog of 796 Utilities Based on a Systematic Review

OBJECTIVES

We performed a systematic review of health state utility values (HSUVs) obtained using the EQ-5D questionnaire for patients with hematologic malignancies.

METHODS

The following databases were searched up to September 2018: MEDLINE, EMBASE, The Cochrane Library, and the EQ-5D publications database on the EuroQol website. Additional references were extracted from reviewed articles. Only studies presenting EQ-Index results were incorporated. In view of the heterogeneity across the included publications, we limited ourselves to a narrative synthesis of original HSUVs found.

RESULTS

Fifty-nine studies (described in 63 articles) met the inclusion criteria. Data from 21 635 respondents provided 796 HSUV estimates for hematologic malignancy patients. EQ-Index scores ranged from -0.025 to 0.980. The most represented area was multiple myeloma (4 studies, 11 112 patients, and 249 HSUVs). In clinical areas such as chronic myeloid leukemia, acute myeloid leukemia, chronic lymphocytic leukemia, non-Hodgkin lymphoma, and mantle cell lymphoma, we described over 50 health utilities in each. In contrast, we identified only 13 HSUVs (based on 4 studies and the data of 166 patients) for Hodgkin lymphoma. Areas without EQ-5D-based health utilities comprised: polycythemia vera, primary myelofibrosis, essential thrombocythemia, mastocytosis, myeloid sarcoma, chronic myelomonocytic, eosinophilic leukemia, and neutrophilic leukemia.

CONCLUSIONS

There is a wide range of HSUVs available for hematologic cancer patients with different indications. The review provides a catalog of utility values for use in cost-effectiveness models for hematologic malignancies.

'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.

Using the Fatigue Severity Scale to inform healthcare decision-making in multiple sclerosis: mapping to three quality-adjusted life-year measures (EQ-5D-3L, SF-6D, MSIS-8D)

Background

Fatigue has a major influence on the quality of life of people with multiple sclerosis. The Fatigue Severity Scale is a frequently used patient-reported measure of fatigue impact, but does not generate the health state utility values required to inform cost-effectiveness analysis, limiting its applicability within decision-making contexts. The objective of this study was to use statistical mapping methods to convert Fatigue Severity Scale scores to health state utility values from three preference-based measures: the EQ-5D-3L, SF-6D and Multiple Sclerosis Impact Scale-8D.

Methods

The relationships between the measures were estimated through regression analysis using cohort data from 1056 people with multiple sclerosis in South West England. Estimation errors were assessed and predictive performance of the best models as tested in a separate sample (n = 352).

Results

For the EQ-5D and the Multiple Sclerosis Impact Scale-8D, the best performing models used a censored least absolute deviation specification, with Fatigue Severity Scale total score, age and gender as predictors. For the SF-6D, the best performing model used an ordinary least squares specification, with Fatigue Severity Scale total score as the only predictor.

Conclusions

Here we present algorithms to convert Fatigue Severity Scales scores to health state utility values based on three preference-based measures. These values may be used to estimate quality-adjusted life-years for use in cost-effectiveness analyses and to consider the health-related quality of life of people with multiple sclerosis, thereby informing health policy decisions.

Electronic supplementary material

The online version of this article (10.1186/s12955-019-1205-y) contains supplementary material, which is available to authorized users.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

Everolimus, lutetium-177 DOTATATE and sunitinib for advanced, unresectable or metastatic neuroendocrine tumours with disease progression: a systematic review and cost-effectiveness analysis

BACKGROUND

Neuroendocrine tumours (NETs) are a group of heterogeneous cancers that develop in cells in the diffuse neuroendocrine system.

OBJECTIVES

To estimate the clinical effectiveness of three interventions [everolimus (Afinitor^®; Novartis International AG, Basel, Switzerland), lutetium-177 DOTATATE (177Lu-DOTATATE) (Lutathera^®; Imaging Equipment Ltd, Radstock, UK) and sunitinib (Sutent^®; Pfizer Inc., New York, NY, USA)] for treating unresectable or metastatic NETs with disease progression and establish the cost-effectiveness of these interventions.

DATA SOURCES

The following databases were searched from inception to May 2016: MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, MEDLINE Daily, Epub Ahead of Print, EMBASE, Cochrane Central Register of Controlled Trials and Web of Science.

REVIEW METHODS

We systematically reviewed the clinical effectiveness and cost-effectiveness literature on everolimus, 177Lu-DOTATATE and sunitinib for treating advanced, unresectable or metastatic progressive NETs. The following NET locations were considered separately: pancreas, gastrointestinal (GI) tract and lung, and GI tract (midgut only). We wrote a survival partition cohort-based economic evaluation in Microsoft Excel^® 2013 (Microsoft Corporation, Redmond, WA, USA) from the UK NHS and Personal Social Services perspective. This comprised three health states: (1) progression-free survival (PFS), (2) progressed disease and (3) death.

RESULTS

Three randomised controlled trials (RCTs), RADIANT-3 [RAD001 in Advanced Neuroendocrine Tumors, Third Trial; pancreatic NETs (pNETs): everolimus vs. best supportive care (BSC)], A6181111 (pNETs: sunitinib vs. BSC) and RADIANT-4 (RAD001 in Advanced Neuroendocrine Tumors, Fourth Trial; GI and lung NETs: everolimus vs. BSC), met the inclusion criteria for the clinical effectiveness systematic review. The risk of bias was low. Although the NETTER-1 (Neuroendocrine Tumors Therapy) RCT, of 177Lu-DOTATATE plus 30 mg of octreotide (Sandostatin^®, Novartis) compared with 60 mg of octreotide, was excluded from the review, we nonetheless present the results of this trial, as it informs our estimate of the cost-effectiveness of 177Lu-DOTATATE. The pNETs trials consistently found that the interventions improved PFS and overall survival (OS) compared with BSC. Our indirect comparison found no significant difference in PFS between everolimus and sunitinib. Estimates of OS gain were confounded because of high rates of treatment switching. After adjustment, our indirect comparison suggested a lower, but non-significant, hazard of death for sunitinib compared with everolimus. In GI and lung NETs, everolimus significantly improved PFS compared with BSC and showed a non-significant trend towards improved OS compared with BSC. Adverse events were more commonly reported following treatment with targeted interventions than after treatment with BSC. In the base case for pNETs, assuming list prices, we estimated incremental cost-effectiveness ratios (ICERs) for everolimus compared with BSC of £45,493 per quality-adjusted life-year (QALY) and for sunitinib compared with BSC of £20,717 per QALY. These ICERs increased substantially without the adjustment for treatment switching. For GI and lung NETs, we estimated an ICER for everolimus compared with BSC of £44,557 per QALY. For GI (midgut) NETs, the ICERs were £199,233 per QALY for everolimus compared with BSC and £62,158 per QALY for a scenario analysis comparing 177Lu-DOTATATE with BSC. We judge that no treatment meets the National Institute for Health and Care Excellence's (NICE) end-of-life criteria, although we cannot rule out that sunitinib in the A6181111 trial does.

LIMITATIONS

A RCT with included comparators was not identified for 177Lu-DOTATATE. The indirect treatment comparison that our economic analysis was based on was of a simple Bucher type, unadjusted for any differences in the baseline characteristics across the two trials.

CONCLUSIONS

Given NICE's current stated range of £20,000-30,000 per QALY for the cost-effectiveness threshold, based on list prices, only sunitinib might be considered good value for money in England and Wales.

FUTURE WORK

Further analysis of individual patient data from RADIANT-3 would allow assessment of the robustness of our findings. The data were not made available to us by the company sponsoring the trial.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016041303.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

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.

PREDOMOS study, impact of a social intervention program for socially isolated elderly cancer patients: update to the study protocol for a randomized controlled trial

Background

Social isolation potentiates the risk of death by cancer in the older cancer patient population. The PREDOMOS study investigates the impact of establishing a Program of Social intervention associated with techniques of Domotic and Remote assistance on the improvement of quality of life of older isolated patients, treated for locally advanced or metastatic cancer. This paper updates the pilot trial protocol.

Methods/design

The original protocol was published in Trials, accessible at https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-1894-7.

This update reports on the eligibility criteria expansion and on the adjunction of a cost-utility analysis. We widened the eligible population to patients with locally advanced or metastatic cancer including malignant hemopathies (except acute myeloid leukemia) and to patients in the first and second lines of oncologic treatment. We restricted the inclusion to patients with a Mini Mental State Examination score strictly over 24. In addition to the secondary outcomes outlined in the protocol, a medico-economic analysis has been added to evaluate both the health benefits and costs of the two strategies and calculate the incremental cost-utility ratio of the innovative program assessed, compared to the standard practice.

Trial registration

ClinicalTrials.gov, NCT02829762. Registered on 29 June 2016.

Health-related quality of life and cost-effectiveness analysis of gum chewing in patients undergoing colorectal surgery: results of a randomized controlled trial

BACKGROUND

Postoperative ileus (POI) and anastomotic leakage (AL) following colorectal surgery severely increase healthcare costs and decrease quality of life. This study evaluates the effects of reducing POI and AL via perioperative gum chewing compared to placebo (control) on in-hospital costs, health-related quality of life (HRQoL), and assesses cost-effectiveness.

METHODS

In patients undergoing elective, open colorectal surgery, changes in HRQoL were assessed using EORTC-QLQ-C30 questionnaires and costs were estimated from a hospital perspective. Incremental cost-effectiveness ratios were estimated.

RESULTS

In 112 patients, mean costs for ward stay were significantly lower in the gum chewing group when compared to control (€3522 (95% CI €3034-€4010) versus €4893 (95% CI €3843-€5942), respectively, p = .020). No differences were observed in mean overall in-hospital costs, or in mean change in any of the HRQoL scores or utilities. Gum chewing was dominant (less costly and more effective) compared to the control in more than 50% of the simulations for both POI and AL.

CONCLUSION

Reducing POI and AL via gum chewing reduced costs for ward stay, but did not affect overall in-hospital costs, HRQoL, or mapped utilities. More studies with adequate sample sizes using validated questionnaires at standardized time points are needed.

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.

Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

PURPOSE

The purpose of the study was to compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities, and to estimate the mapping algorithms that best predict these health state utilities.

METHODS

772 cancer patients described their health along the cancer-specific instrument (QLQ-C30) and two generic preference-based instruments (EQ-5D-5L and SF-6D). Seven alternative regression models were applied: ordinary least squares, generalized linear model, extended estimating equations (EEE), fractional regression model, beta binomial (BB) regression, logistic quantile regression and censored least absolute deviation. Normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), r-squared (r²) and concordance correlation coefficient (CCC) were used as model performance criteria. Cross-validation was conducted by randomly splitting internal dataset into two equally sized groups to test the generalizability of each model.

RESULTS

In predicting EQ-5D-5L utilities, the BB regression performed best. It gave better predictive accuracy in terms of all criteria in the full sample, as well as in the validation sample. In predicting SF-6D, the EEE performed best. It outperformed in all criteria: NRMSE = 0.1004, NMAE = 0.0798, CCC = 0.842 and r² = 72.7% in the full sample, and NRMSE = 0.1037, NMAE = 0.0821, CCC = 0.8345 and r² = 71.4% in cross-validation.

CONCLUSIONS

When only QLQ-C30 data are available, mapping provides an alternative approach to obtain health state utility data for use in cost-effectiveness analyses. Among seven alternative regression models, the BB and the EEE gave the most accurate predictions for EQ-5D-5L and SF-6D, respectively.

Assessment of outcome measures for cost-utility analysis in depression: mapping depression scales onto the EQ-5D-5L

BACKGROUND

Many clinical studies including mental health interventions do not use a health state utility instrument, which is essential for producing quality-adjusted life years. In the absence of such utility instrument, mapping algorithms can be applied to estimate utilities from a disease-specific instrument.AimsWe aim to develop mapping algorithms from two widely used depression scales; the Depression Anxiety Stress Scales (DASS-21) and the Kessler Psychological Distress Scale (K-10), onto the most widely used health state utility instrument, the EQ-5D-5L, using eight country-specific value sets.

METHOD

A total of 917 respondents with self-reported depression were recruited to describe their health on the DASS-21 and the K-10 as well as the new five-level version of the EQ-5D, referred to as the EQ-5D-5L. Six regression models were used: ordinary least squares regression, generalised linear models, beta binomial regression, fractional logistic regression model, MM-estimation and censored least absolute deviation. Root mean square error, mean absolute error and r2 were used as model performance criteria to select the optimal mapping function for each country-specific value set.

RESULTS

Fractional logistic regression model was generally preferred in predicting EQ-5D-5L utilities from both DASS-21 and K-10. The only exception was the Japanese value set, where the beta binomial regression performed best.

CONCLUSIONS

Mapping algorithms can adequately predict EQ-5D-5L utilities from scores on DASS-21 and K-10. This enables disease-specific data from clinical trials to be applied for estimating outcomes in terms of quality-adjusted life years for use in economic evaluations.Declaration of interestNone.

Direct Mapping of the QLQ-C30 to EQ-5D Preferences: A Comparison of Regression Methods

BACKGROUND

Several mapping or cross-walking algorithms for deriving utilities from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire for Cancer (EORTC QLQ-C30) scores have been published in recent years. However, the large majority used ordinary least squares (OLS) regression, which proved to be not very accurate because of the specifics of the quality-of-life measures.

OBJECTIVE

Our objective was to compare regression methods that have been used to map EuroQol 5 Dimensions 3 Levels (EQ-5D-3L) utility values from the general EORTC QLQ-C30 using OLS as a benchmark while fixing the number of explanatory variables and to explore an alternative three-part model.

METHODS

We conducted a regression analysis of predicted EQ-5D-3L utilities generated using data from an observational study in ambulatory patients with non-small-cell lung cancer in a Toronto hospital. Six alternative regression methods were compared with a simple OLS regression as benchmark. The six alternative regression models were Tobit, censored least absolute deviation, normal mixture, beta, zero-one inflated beta and a mix of piecewise OLS and logistic regression.

RESULTS

The best predictive fit was obtained by a mix of OLS regression(s) for utilities lower than 1 with a cut-off point of 0.50 and a separate binary logistic regression for utilities equal to one. Zero-one inflated beta regression was also promising. However, OLS regression proved to be the most accurate for the mean. The prediction of utilities equal to one was poor in all regression approaches.

CONCLUSIONS

Three-part regression methods that separately target low, medium and high (<0.50, 0.51-0.99 or 1) utilities seem to have better prediction power than OLS with EQ-5D-3L data, although OLS also seems quite robust. Exploration of three-part approaches compared with single (OLS) regression should be further tested in other similar datasets or using individual pooled data from various clinical or observational studies. The use of alternative goodness-of-fit measures for mapping studies and their influence on the choice of the best performing methods should also be investigated.

Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets

PURPOSE

To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.

METHODS

Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r². Cross-validation with fivefold approach was conducted to test the generalizability of each model.

RESULTS

The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r² was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models.

CONCLUSIONS

The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer

AIMS

The primary aim of this study was to perform a mapping of the EORTC-QLQ-C30 scores to EQ-5D-3L for the SIRFLOX study; a large dataset of patients with previously untreated liver-only or liver-dominant metastatic colorectal cancer (mCRC). A secondary aim was to compare the predictive validity of existing mappings from EORTC-QLQ-C30 to EQ-5D-3L conducted in other cancers.

METHODS AND MATERIALS

Questionnaires (completed within 529 patients) were used in a linear mixed regression to model EQ-5D-3L utility values (scored using the UK tariff) as a function of the five function scores, nine symptom scores, and the global score from the EORTC-QLQ-C30 questionnaire. A Tobit regression was also performed. The mean EQ-5D-3L values for the SIRFLOX trial were calculated and compared with predicted EQ-5D-3L values derived using published mapping algorithms.

RESULTS

The linear mixed regression model provided a satisfactory mapping between the EORTC-QLQ-C30 and the EQ-5D-3L, whilst the Tobit model did not perform as well. When utilities from the SIRFLOX data were calculated with previously published mapping studies, three out of five studies performed well (< 10% mean difference).

LIMITATIONS

The main limitation of the study was the lack of meaningful observations post-progression (67 paired observations). For this reason, this study was unable to test whether the mapping holds by disease stage. Additionally, although the study adds to the literature of mappings to the EQ-5D-3L, it is not known how results would differ using the EQ-5D-5L.

CONCLUSION

This study is the first of its kind in liver-only or liver-dominant mCRC, and mCRC in general. The mapping constructed showed a good fit to the data and provides practitioners with an additional mapping between EORTC-QLQ-C30 to EQ-5D-3L using a large dataset (529 patients, 707 paired observations). The study also confirmed the generalizability of mappings published by Proskorovsky, Kontodimopoulos, and Longworth to liver-only or liver-dominant mCRC.

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.

Impact on the Incremental Cost-Effectiveness Ratio of Using Alternatives to EQ-5D in a Markov Model for Multiple Sclerosis

OBJECTIVES

This study responds to a request in the National Institute for Health and Care Excellence (NICE) guidance to assess the impact of using alternative sources of utility values, applied to multiple sclerosis (MS).

METHODS

Incremental cost-effectiveness ratios (ICERs) were calculated using utility values based on UK and Dutch values of EQ-5D, two UK mappings and one Dutch mapping of EQ-5D and two condition-specific instruments: the UK eight-dimensional Multiple Sclerosis Impact Scale (MSIS-8D) and the Dutch Multiple Sclerosis Impact Scale Preference-Based Measure (MSIS-PBM). Deterministic and Monte-Carlo simulation-based ICERs were estimated for glatiramer acetate versus symptom management using a lifetime Markov model.

RESULTS

For both UK and Dutch perspectives, mapped and condition-specific utility values expressed significantly higher quality of life for the worst health state of the model than did EQ-5D. The ICER of glatiramer acetate with EQ-5D was US$182,291 for The Netherlands and US$153,476 for the UK. Ratios for mapped and condition-specific utilities were between 20 and 60 % higher.

CONCLUSION

The overestimation of quality of life of patients with MS by mapped EQ-5D or condition-specific utility values, relative to observed EQ-5D, increases the ICER substantially in a lifetime Markov model.

Mapping of the OAB-SF Questionnaire onto EQ-5D in Spanish Patients with Overactive Bladder

BACKGROUND AND OBJECTIVE

Mapping disease-specific measures onto generic preference-based indexes allows estimating utility values in specific conditions to determine gain of quality-adjusted-life-years when the status of condition varies. The aim of this study was to map a disease specific scale, the Overactive Bladder Questionnaire 5-dimensional health classification system (OAB-5D) derived from the Overactive Bladder questionnaire-Short Form (OABq-SF), onto a preference-based scale, the EuroQol-5D (EQ-5D), in a sample of patients with overactive bladder (OAB) in a Spanish population.

METHOD

A survey addressed to value the health states was conducted among 246 patients at 18 clinics of urology from Spain. A total of 43 out of 243 possible health states have been valued, using VAS (Visual Analog Scale) and TTO (time trade-off) techniques. In addition, ordinary least squares (OLS), generalized linear models (GLM) and Tobit models were estimated. Resulting models were compared and the best one was selected in terms of goodness of fit measures, attribute sign, coefficient magnitude, and statistical significance of regression coefficients. Finally, the internal validity of the best model was calculated by bootstrap resampling.

RESULTS

The best model to map the OAB-5D onto EQ-5D could be estimated and the stability of parameter estimations was proved. The mentioned model estimated through OLS regression attained R (2) value of 0.892, with the aggregated data; with GLM (efficient maximum likelihood regression), Pearson χ (2) of 15.3 has been obtained; AIC (Akaike information criterion) = -550.9 and BIC (Bayesian information criterion) = -475.4. OLS model included the following OABq-SF items (and range of weights): A1 (0.102, 0.216); A3 (0.070, 0.171); B3 (0.071, 0.078); B1 (0.076, 0.136); B2 (-0.132, -0.028).

CONCLUSION

It is possible to map the OAB-5D scores onto EQ-5D in the Spanish population, allowing estimating EQ-5D utility scores from OAB specific health conditions.

Which Questionnaire Should Be Used to Measure Quality-of-Life Utilities in Patients with Acute Leukemia? An Evaluation of the Validity and Interpretability of the EQ-5D-5L and Preference-Based Questionnaires Derived from the EORTC QLQ-C30

OBJECTIVES

The aim of this study was to assess the validity and interpretability of different preference-based questionnaires (generic 5-level EuroQol five-dimensional questionnaire [EQ-5D-5L], cancer-specific Quality of Life Questionnaire Preference-Based Measure, and European Organization of Randomized Controlled Trials 8 Dimension [EORTC-8D]) in patients with acute leukemia.

METHODS

Patients who participated in Hemato-Oncologie voor Volwassenen Nederland (HOVON - the Haemato Oncology Foundation for Adults in the Netherlands) clinical trials between 1999 and 2011 at a single hospital were invited to complete the questionnaires. Interpretability was evaluated by the frequency of incomplete data and highest and lowest possible scores. Content validity was evaluated by exploring the health-related quality-of-life domains included in the questionnaires. Construct validity was assessed using correlations with other quality-of-life scales (EQ-visual analogue scale score and global quality-of-life scale of the EORTC Quality of Life Questionnaire) and ability to distinguish between patients with different health statuses.

RESULTS

Questionnaires were returned by 89% (111 of 125) of the patients. Six to seven respondents did not return full questionnaires. Perfect health on the EQ-5D-5L was reported by 32 respondents and many of them (N = 17) did report health problems on other questionnaires. All questionnaires were strongly correlated (range 0.61-0.78) with other quality-of-life scales and yielded substantially different utility values for patients with different health statuses. Nevertheless, the disease-specific preference-based questionnaires showed greater discriminatory power.

CONCLUSIONS

Although the Quality of Life Questionnaire Preference-Based Measure and the EORTC-8D appear to have better validity, this study does not provide any strong evidence against the use of the EQ-5D-5L for measuring quality-of-life utilities in acute leukemia. However, our findings need to be confirmed in larger longitudinal studies.

Cost-Effectiveness Model for Chemoimmunotherapy Options in Patients with Previously Untreated Chronic Lymphocytic Leukemia Unsuitable for Full-Dose Fludarabine-Based Therapy

OBJECTIVES

To evaluate the cost-effectiveness of treatment with anti-CD20 monoclonal antibody obinutuzumab plus chlorambucil (GClb) in untreated patients with chronic lymphocytic leukemia unsuitable for full-dose fludarabine-based therapy.

METHODS

A Markov model was used to assess the cost-effectiveness of GClb versus other chemoimmunotherapy options. The model comprised three mutually exclusive health states: "progression-free survival (with/without therapy)", "progression (refractory/relapsed lines)", and "death". Each state was assigned a health utility value representing patients' quality of life and a specific cost value. Comparisons between GClb and rituximab plus chlorambucil or only chlorambucil were performed using patient-level clinical trial data; other comparisons were performed via a network meta-analysis using information gathered in a systematic literature review. To support the model, a utility elicitation study was conducted from the perspective of the UK National Health Service.

RESULTS

There was good agreement between the model-predicted progression-free and overall survival and that from the CLL11 trial. On incorporating data from the indirect treatment comparisons, it was found that GClb was cost-effective with a range of incremental cost-effectiveness ratios below a threshold of £30,000 per quality-adjusted life-year gained, and remained so during deterministic and probabilistic sensitivity analyses under various scenarios.

CONCLUSIONS

GClb was estimated to increase both quality-adjusted life expectancy and treatment costs compared with several commonly used therapies, with incremental cost-effectiveness ratios below commonly referenced UK thresholds. This article offers a real example of how to combine direct and indirect evidence in a cost-effectiveness analysis of oncology drugs.

Health Utility Values Associated with Surgical Site Infection: A Systematic Review

BACKGROUND

Surgical site infection (SSI) is a costly postoperative complication whose impact on patients' health-related quality of life is highly uncertain and has not been summarized to date.

OBJECTIVE

The objective was to summarize the evidence base on SSI health utility values reported in patient-level studies and decision models.

METHODS

A systematic review of SSI utility values reported in patient-level and decision modeling studies was carried out. Studies in which utility values for SSI were either invoked (e.g., model-based economic evaluations) or elicited (e.g., valuation exercises), or at least one non-preference-based instrument was administered to patients with SSI after open surgery were included. Mapping algorithms were used, where appropriate, to calculate utilities from primary data. Results were summarized narratively, and the quality of the utility values used in the included modeling studies was assessed.

RESULTS

Of 6552 records identified in the database search, 28 studies were included in the review: 19 model-based economic evaluations and 9 patient-level studies. SSI utility decrements ranged from 0.04 to 0.48, of which 19 ranged from 0.1 to 0.3. SSI utility decrements could be calculated for three patient-level studies, and their values ranged from 0.05 (7 days postoperatively) to 0.124 (1 year postoperatively). In most modeling studies, SSI utilities were informed by authors' assumptions or by secondary sources.

CONCLUSIONS

SSI may substantially affect patients' health utility and needs to be considered when modeling decision problems in surgery. The evidence base for SSI utilities is of questionable quality and skewed toward orthopedic surgery. Further research must concentrate on producing reliable estimates for patients without orthopedic problems.

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.

Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

OBJECTIVE

Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities.

METHODS

Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R(2)) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models.

RESULTS

Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair.

CONCLUSION

HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations.

Quality of life of patients with chronic lymphocytic leukaemia in the Netherlands: results of a longitudinal multicentre study

Purpose

To describe the health-related quality of life (HRQoL) of an unselected population of patients with chronic lymphocytic leukaemia (CLL) including untreated patients.

Methods

HRQoL was measured by the EORTC QLQ-C30 including the CLL16 module, EQ-5D, and VAS in an observational study over multiple years. All HRQoL measurements per patient were connected and analysed using area under the curve analysis over the entire study duration. The total patient group was compared with the general population, and three groups of CLL patients were described separately, i.e. patients without any active treatment (“watch and wait”), chlorambucil treatment only, and patients with other treatment(s).

Results

HRQoL in the total group of CLL patients was compromised when compared with age- and gender-matched norm scores of the general population. CLL patients scored statistically worse on the VAS and utility score of the EQ-5D, all functioning scales of the EORTC QLQ-C30, and the symptoms of fatigue, dyspnoea, sleeping disturbance, appetite loss, and financial difficulties. In untreated patients, the HRQoL was slightly reduced. In all treatment stages, HRQoL was compromised considerably. Patients treated with chlorambucil only scored worse on the EORTC QLQ-C30 than patients who were treated with other treatments with regard to emotional functioning, cognitive functioning, bruises, uncomfortable stomach, and apathy.

Conclusions

CLL patients differ most from the general population on role functioning, fatigue, concerns about future health, and having not enough energy. Once treatment is indicated, HRQoL becomes considerably compromised. This applies to all treatments, including chlorambucil, which is considered to be a mild treatment.

Electronic supplementary material

The online version of this article (doi:10.1007/s11136-015-1039-y) contains supplementary material, which is available to authorized users.

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.

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

Background

In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published.

Methods

Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed.

Results

The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset.

Conclusions

This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

Electronic supplementary material

The online version of this article (doi:10.1186/s12955-014-0196-y) contains supplementary material, which is available to authorized users.

Predicting health utilities for children with autism spectrum disorders

Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ ≤ 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions.

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 EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma

BACKGROUND

In 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.

METHODS

We 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.

RESULTS

Mapping 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.

CONCLUSIONS

This 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.

Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS)

BACKGROUND

The Prostate Cancer Index (PCI) is a health profile instrument that measures health-related quality of life with six subscales: urinary, sexual, and bowel function and bother. The Patient-Oriented Prostate Utility Scale (PORPUS-U) measures utility (0=dead and 1=full health). Utility is a preference-based approach to measure health-related quality of life, required for decision analyses and cost-effectiveness analyses. We developed a function to estimate PORPUS-U utilities from PCI scores.

METHODS

The development data set included 676 community-dwelling prostate cancer (PC) survivors who completed the PCI and PORPUS-U by mail. We fit three linear regression models: one used original PORPUS-U scores and two used log-transformed PORPUS-U scores, one with a hierarchy constraint and one without. The model selection was performed using stepwise selection and fivefold cross validation. The validation data included 248 PC outpatients with three assessments on the PCI and PORPUS-U. Scores were retransformed for validation, with Duan's smearing estimator applied to correct potential bias. The predictive ability of the models was assessed with R(2), root mean square error (RMSE) and by comparing predicted and observed utilities.

RESULTS

The best-fitting model used the log-transformed PORPUS-U with no hierarchy constraint. The R(2) was 0.72. The RMSE ranged from 0.040 to 0.061 for the three validation data sets. Differences between predicted and observed utilities ranged from 0.000 to 0.006 but predicted utilities overestimated the lowest 5% of observed PORPUS-U scores and underestimated the highest observed scores.

CONCLUSIONS

Our algorithm can calculate PORPUS-U utility scores from PCI scores, thus supplementing descriptive quality of life measures with utility scores in PC patients. Utilities derived from mapping algorithms are useful for assigning utility to groups of patients but are less accurate at predicting utility of individual patients. We are exploring statistical methods to improve the mapping of utilities from descriptive instruments.

Mapping the Gastrointestinal Quality of Life Index to short-form 6D utility scores

PURPOSE

The Gastrointestinal Quality of Life Index (GIQLI) is a disease-specific quality of life instrument that is commonly used in gastrointestinal disorders. However, no preference weights, which are used to calculate quality-adjusted life years for cost-effectiveness analyses, for this instrument have been assigned. Therefore, the objective of this study was to develop a mapping function to derive utility scores for the GIQLI.

METHODS

Patients undergoing elective laparoscopic cholecystectomy completed the short-form 6D (SF-6D), a preference-weighted utility measure, and the GIQLI at baseline and at 1 wk and 1 mo postoperatively. The responsiveness of the SF-6D to expected postoperative changes as well as the correlation between the SF-6D and the GIQLI was investigated. Ordinary least squares regression was performed to derive a mapping function using the baseline values, which was then validated using postoperative values. Adjusted r(2), mean absolute error, and root mean square error was used to determine model performance.

RESULTS

A total of 50 patients were included in the study (mean age 51.1 [standard deviation 16.8], 28% male). The SF-6D and GIQLI domains were responsive to expected postoperative changes, and there was high correlation between the SF-6D and four of the five GIQLI domains (excluding treatment effects). The final mapping function explained 63% of the variance in the derivation sample but had relatively high mean absolute error (0.075 and 0.067 in the 1-wk and 1-mo samples, respectively) and adjusted root mean square error (13.1% and 12.0% in the 1-wk and 1-mo samples, respectively).

CONCLUSIONS

The GIQLI can be accurately mapped to SF-6D preference weights at the group level but may be too inexact at the individual level.

Predicting SF-6D from the European Organization for Treatment and Research of Cancer Quality of Life Questionnaire scores in patients with colorectal cancer

OBJECTIVES

To develop a mapping model for estimating six-dimensional health state short form (SF-6D) utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaires (QLQ-C30 and QLQ-CR29) scores in patients with colorectal cancer (CRC), with and without adjustment for clinical and demographic characteristics.

METHODS

Ordinary least squares regression models were applied to a cross-sectional data set of 216 patients with CRC collected from a regional hospital in Hong Kong. Item responses or scale scores of cancer-specific (QLQ-C30) and colorectal-specific health-related quality-of-life (QLQ-CR38/CR29) data and selected demographic and clinical characteristics of patients were used to predict the SF-6D scores. Model goodness of fit was examined by using exploratory power (R(2) and adjusted R(2)), Akaike information criterion, and Bayesian information criterion, and predictive performance was evaluated by using root mean square error, mean absolute error, and Spearman's correlation coefficients between predicted and observed SF-6D scores. Models were validated by using an independent data set of 56 patients with CRC.

RESULTS

Both scale and item response models explained more than 67% of the variation in SF-6D scores. The best-performing model based on goodness of fit (R(2) = 75.02%), predictive ability in the estimation (root mean square error = 0.080, mean absolute error = 0.065), and validation data set prediction (root mean square error = 0.103, mean absolute error = 0.081) included variables of main and interaction effects of the QLQ-C30 supplemented by QLQ-CR29 subset scale responses and a demographic (sex) variable.

CONCLUSIONS

SF-6D scores can be predicted from QLQ-C30 and QLQ-CR38/CR29 scores with satisfactory precision in patients with CRC. The mapping model can be applied to QLQ-C30 and QLQ-CR38/CR29 data sets to produce utility scores for the appraisal of clinical interventions targeting patients with CRC using economic evaluation.

Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients

Background

The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) is the instrument most frequently used to measure quality of life in cancer patients, whereas the EQ-5D is widely used to measure and evaluate general health status. Although the EORTC QLQ-C30 has been mapped to EQ-5D utilities, those studies were limited to patients with a single type of cancer. The present study aimed to develop a mapping relationship between the EORTC QLQ-C30 and EQ-5D-based utility values at the individual level.

Methods

The model was derived using patients with different types of cancer who were receiving chemotherapy. The external validation set comprised outpatients with colon cancer. Ordinary least squares regression was used to estimate the EQ-5D index from the EORTC QLQ-C30 results. The predictability, goodness of fit, and signs of the estimated coefficients of the model were assessed. Predictive ability was determined by calculating the mean absolute error, the estimated proportions with absolute errors > 0.05 and > 0.1, and the root-mean-squared error (RMSE).

Results

A model that included global health, physical, role, emotional functions, and pain was optimal, with a mean absolute error of 0.069 and an RMSE of 0.095 (normalized RMSE, 8.1%). The explanatory power of this model was 51.6%. The mean absolute error was higher for modeled patients in poor health.

Conclusions

This mapping algorithm enabled the EORTC QLQ-C30 to be converted to the EQ-5D utility index to assess cancer patients in Korea.

Comparison of generic, condition-specific, and mapped health state utility values for multiple myeloma cancer

OBJECTIVE

Resource allocation informed by cost-utility analysis requires that the benefits be comparable across patient groups and interventions. One option is to recommend the use of one generic utility measure, but this raises the issue of comparability when the preferred measure is inappropriate or unavailable. Many cancer trials do not include generic measures such as the EuroQol five-dimensional (EQ-5D) questionnaire and instead include condition-specific measures and use these to generate utility estimates. We analyze the comparability of generic, condition-specific, and mapped utility values for a multiple myeloma cancer patient data set.

METHODS

Generic EQ-5D, condition-specific EORTC-8D, and EQ-5D utility values mapped from the EORTC QLQ-C30 were compared by using psychometric and statistical analysis to determine discrimination across severity groups, responsiveness, and agreement.

RESULTS

Generic, condition-specific, and mapped utility estimates were responsive over time and show discriminative validity. The EQ-5D had higher responsiveness and detected a greater change across severity groups and treatment periods than did the EORTC-8D but has a higher proportion of responses at full health (12.8%). Differences in the EQ-5D and the EORTC-8D were due at least in part to differences in the classification system. Mapped EQ-5D estimates had a smaller SD and do not reflect the severe range of health states reported by using the EQ-5D.

CONCLUSIONS

Our findings suggest that condition-specific EORTC-8D or mapped EQ-5D utility estimates are broadly comparable to directly obtained EQ-5D utilities for a multiple myeloma patient data set. However, EORTC-8D estimates captured changes in quality of life for patients in mild health states that were not captured by the EQ-5D, but estimated lower utility gains than did the use of the EQ-5D directly.

Using the Multiple Sclerosis Impact Scale to estimate health state utility values: mapping from the MSIS-29, version 2, to the EQ-5D and the SF-6D

OBJECTIVES

The 29-item Multiple Sclerosis Impact Scale (MSIS-29) is a psychometrically validated patient-reported outcome measure increasingly used in trials of treatments for multiple sclerosis. However, it is non-preference-based and not amenable for use across policy decision-making contexts. Our objective was to statistically map from the MSIS-29, version 2, to the EuroQol five-dimension (EQ-5D) and the six-dimension health state short form (derived from short form 36 health survey) (SF-6D) to estimate algorithms for use in cost-effectiveness analyses.

METHODS

The relationships between MSIS-29, version 2, and EQ-5D and SF-6D scores were estimated by using data from a cohort of people with multiple sclerosis in South West England (n=672). Six ordinary least squares (OLS), Tobit, and censored least adjusted deviation (CLAD) regression analyses were conducted on estimation samples, including the use of subscale and item scores, squared and interaction terms, and demographics. Algorithms from models with the smallest estimation errors (mean absolute error [MAE], root mean square error [RMSE], normalized RMSE) were then assessed by using separate validation samples.

RESULTS

Tobit and CLAD. For the EQ-5D, the OLS models including subscale squared terms, and item scores and demographics performed comparably (MAE 0.147, RMSE 0.202 and MAE 0.147, RMSE 0.203, respectively), and estimated scores well up to 3 years post-baseline. Estimation errors for the SF-6D were smaller (OLS model including squared terms: MAE 0.058, RMSE 0.073; OLS model using item scores and demographics: MAE 0.059, RMSE 0.08), and the errors for poorer health states found with the EQ-5D were less pronounced.

CONCLUSIONS

We have provided algorithms for the estimation of health state utility values, both the EQ-5D and SF-6D, from scores on the MSIS-29, version 2. Further research is now needed to determine how these algorithms perform in practical decision-making contexts, when compared with observed EQ-5D and SF-6D values.

An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences

BACKGROUND

Although cancer-specific Health-related Quality-of-Life measures are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used, which allow the calculation of a Utility weight suitable for estimating Quality-adjusted Life-Years gained.

OBJECTIVE

To test the external validity of a previously published mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D-derived utilities by predicting EQ-5D utilities from QLQ-C30 scores.

STUDY DESIGN AND METHODS

Comparative retrospective data analysis of four multicentre, prospective clinical trials in Breast, Multiple Myeloma, Non-Hodgkin Lymphoma and Non-Small-Cell Lung cancer patients with, respectively, 219, 172, 132 and 172 patients. Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores.

RESULTS

Although the internal predictive power of a previously published mapping equation was high, its external validity when tested on a set of unrelated external data sets in other cancers proved to underestimate both the mean and variance of the mapped EQ-5D utilities. Furthermore, it appears that the relationship between QLQ-C30 scores and EQ-5D values is not stable across the different data sets.

CONCLUSIONS

Validation of the proposed algorithm in other external clinical data sets should be encouraged as well as the application of other more complex mapping methods to enhance accuracy of mapping. In the meanwhile, direct mapping from QLQ-C30 profiles to EQ-5D utilities using published algorithms should be performed with reservations.

Looking at Patients’ Choices through the Lens of Expected Utility

The expected utility framework underlies much research in medical decision making. Because the framework requires decisions to be decomposed into probabilities of states and the values of those states, researchers have investigated the two components separately from each other and from patients’ actual decisions. The authors propose that it would be productive to focus more research on the relationships among risk perceptions, outcome valuations, and choices in the same decision makers. They outline exploratory analyses based on two existing national surveys, the Medical Expenditure Panel Survey and the Joint Canada/United States Survey of Health.

Mapping onto Eq-5 D for patients in poor health

BACKGROUND

An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures. Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values for patients in poor health. Currently there are no guidelines on how to deal with this problem. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions.

METHOD

Three existing datasets are used to estimate mapping algorithms and assess existing mapping algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire (HAQ) onto the EQ-5 D. Separate mapping algorithms are estimated for poor health states. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values.

RESULTS

All mapping algorithms suffer from overprediction of utility values for patients in poor health. The large decrement of reporting 'extreme problems' in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. A HAQ separate algorithm could not be estimated due to data restrictions.

CONCLUSION

Mapping algorithms overpredict utility values for patients in poor health but are used in cost-effectiveness analyses nonetheless. Guidelines can be developed on when the use of a mapping algorithms is inappropriate, for instance through the identification of cut-off points. Cut-off points on a disease specific questionnaire can be identified through association with the causes of overprediction. The cut-off points found in this study represent severely impaired health. Specifying a separate mapping algorithm to predict utility values for individuals in poor health greatly reduces overprediction, but does not fully solve the problem.