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

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.

Mapping the Patient-Reported Outcomes Measurement Information System (PROMIS-29) to EQ-5D-5L

BACKGROUND AND OBJECTIVE

The Patient-Reported Outcomes Measurement Information System (PROMIS-29) is gaining popularity as healthcare system funders increasingly seek value-based care. However, it is limited in its ability to estimate utilities and thus inform economic evaluations. This study develops the first mapping algorithm for estimating EuroQol 5-Dimension 5-Level (EQ-5D-5L) utilities from PROMIS-29 responses using a large dataset and through extensive comparisons between econometric models.

METHODS

An online survey was conducted to collect responses to PROMIS-29 and EQ-5D-5L from the general Australian population (N = 3013). Direct and indirect mapping methods were explored, including linear regression, Tobit, generalised linear model, censored regression model, beta regression (Betamix), the adjusted limited dependent variable mixture model (ALDVMM) and generalised ordered logit. The most robust model was selected by assessing the performance based on average ten-fold cross-validation geometric mean absolute error and geometric mean squared error, the predicted mean, maximum and minimum utilities, as well as the fitting across the entire distribution.

RESULTS

The direct approach using ALDVMM was considered the preferred model based on lowest geometric mean absolute error and geometric mean squared error in cross-validation (0.0882, 0.0299) and its superiority in predicting the actual observed mean, full health states and lower utility extremes. The robustness and precision in prediction across the entire distribution of utilities with ALDVMM suggest it is an accurate and valid mapping algorithm. Moreover, the suggested mapping algorithm outperformed previously published algorithms using Australian data, indicating the validity of this model for economic evaluations.

CONCLUSIONS

This study developed a robust algorithm to estimate EQ-5D-5L utilities from PROMIS-29. Consistent with the recent literature, the ALDVMM outperformed all other econometric models considered in this study, suggesting that the mixture models have relatively better performance and are an ideal candidate model for mapping.

Mapping of the World Health Organization Quality of Life Brief (WHOQOL-BREF) to the EQ-5D-5L in the General Thai Population

PURPOSE

This study aimed at mapping the World Health Organization Quality of Life Brief (WHOQOL-BREF) and the EQ-5D-5L in the general Thai population and to determine the impact on the incremental cost-utility ratio (ICUR) through five hypothetical scenarios.

METHODS

A total of 1,200 Thai participants were randomly allocated into the 'estimation' and 'validation' groups. A curve estimation with nine regression models was performed to identify the best-fit regression model of significant WHOQOL-BREF dimension scores for the EQ-5D-5L index score predictions in the estimation group. The identified model was then used for the calculation of the predicted EQ-5D-5L index scores in the validation group. The percentage change from the hypothetical base-case scenario with predefined parameters was used to determine the impact on the ICUR.

RESULTS

An inverse model was the best-fit regression model to predict the EQ-5D-5L index scores. The absolute difference between the predicted and observed index scores was 0.064, and the percentage of the sample that was mispredicted by ≥ 0.05 and ≥ 0.1 was 43.8% and 16.8%, respectively. Moreover, the percentage change in ICUR ranged between 0.13 and 1.84% from the hypothetical base-case scenario.

CONCLUSIONS

An inverse relationship between the studied scores was identified. The minimal impact on the ICUR suggests that the Health Utility Index of the mapped equation can be applied to economic analyses.

An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values

Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi-attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost-effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient-Reported Outcomes Measurement Information System Global Health 10 (PROMIS-GH10) to the EuroQol five dimensions (EQ-5D-5L). Using data collected from 2015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out-performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out-performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model. Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.

Mapping the FACT-G to EQ-5D-3L utility index in cancer with the Chinese values set

OBJECTIVE

The purpose of this research was to create a function for mapping the cancer-specific instrument (FACT-G) to a preference-based measure (EQ-5D-3L) utility index for health-related quality of life, with utility scores generated using the Chinese value set.

METHOD

A cross-sectional study among 243 Chinese patients with cancer was conducted through EQ-5D-3L and FACT-G questionnaires survey. The EQ-5D-3L utility index values wad predicted based on OLS, GLM, CLAD, and Tobit model regression approaches. The performance and predictive power of each model were also evaluated using r² and adj- r², MAE, RMSE, ICC, and MID. Linear equating was used to avoid regression of the OLS model to mean. The model was validated using a 10-fold cross-validation method.

RESULTS

Among all regression models for the FACT-G, the OLS 5 model predicted mean EQ-5D-3L values the best, in terms of model goodness of fit (r²= 0.6230, r²= 58.93%, MAE = 0.0448, RMSE = 0.0624). The OLS model proved to be the most accurate for the mean, and the linear equating scores were much closer to observed scores.

CONCLUSION

Our results suggest that the best algorithm for FACT-G mapping to EQ-5D-3L utility index is OLS model, based on the survey of Chinese patients with cancer.

HIT-6 and EQ-5D-5L in patients with migraine: assessment of common latent constructs and development of a mapping algorithm

OBJECTIVE

The aims of this study were to assess whether there is a conceptual overlap between the questionnaires HIT-6 and EQ-5D and to develop a mapping algorithm allowing the conversion of HIT-6 to EQ-5D utility scores for Germany.

METHODS

This study used data from an ongoing randomised controlled trial for patients suffering from migraine. We assessed the conceptual overlap between the two instruments with correlation matrices and exploratory factor analysis. Linear regression, tobit, mixture, and two-part models were used for mapping, accounting for repeated measurements, tenfold cross-validation was conducted to validate the models.

RESULTS

We included 1010 observations from 410 patients. The EQ-5D showed a substantial ceiling effect (47.3% had the highest score) but no floor effect, while the HIT-6 showed a very small ceiling effect (0.5%). The correlation between the instruments' total scores was moderate (- 0.30), and low to moderate among each domain (0.021-0.227). The exploratory factor analysis showed insufficient conceptual overlap between the instruments, as they load on different factors. Thus, there is reason to believe that the instruments' domains do not capture the same latent constructs. To facilitate future mapping, we provide coefficients and a variance-covariance matrix for the preferred model, a two-part model with the total HIT-6 score as the explanatory variable.

CONCLUSION

This study showed that the German EQ-5D and the HIT-6 lack the conceptual overlap needed for appropriate mapping. Thus, the estimated mapping algorithms should only be used as a last resort for estimating utilities to be employed in economic evaluations.

Indirect and Direct Mapping of the Cancer-Specific EORTC QLQ-C30 onto EQ-5D-5L Utility Scores

OBJECTIVE

The aim of this study was to develop a response mapping algorithm to predict EQ-5D-5L utilities from European Organisation for Research and Treatment Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) scores and compare performance with direct mapping approaches to identify the best performing algorithm.

METHODS

The Multi-Instrument Comparison dataset contains responses to both the EQ-5D-5L and QLQ-C30 questionnaires from 692 individuals with a broad range of cancers. Response mapping was conducted, fitting ordered logistic regressions to predict response levels for each of the five EQ-5D dimensions and utilities were predicted using the US and Japanese EQ-5D-5L value sets to test the algorithm performance. Various direct mapping models were fitted: ordinary least squares, tobit, two-part (TPM), adjusted limited dependent variable mixture and beta mixture models. Model assessment and recommendations regarding the best mapping algorithm was based on goodness-of-fit statistics, predictive ability (measures of error, distribution of predicted utilities) and in sample cross-validation.

RESULTS

The response mapping model performed well in terms of predictive ability and measurement error using the US or Japanese value set, with mean absolute error ranging from 0.0708 to 0.0988, and comparably to the TPM, which was the best performing direct algorithm.

CONCLUSION

The developed mapping algorithms enable the prediction of EQ-5D-5L utilities from QLQ-C30 scores when EQ-5D-5L data have not been directly collected in clinical trials. The response mapping model offers the possibility of predicting EQ-5D-5L utility values using any national value set and can be generalised to multiple countries and oncology settings.

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

Background

The types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation.

Methods

We use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics.

Results

A four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users.

Conclusion

A four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08964-5.

Adapting preference-based utility measures to capture the impact of cancer treatment-related symptoms

It is important that patient-reported outcome (PRO) measures used to assess cancer therapies adequately capture the benefits and risks experienced by patients, particularly when adverse event profiles differ across therapies. This study explores the case for augmenting preference-based utility measures to capture the impact of cancer treatment-related symptoms. Additional cancer treatment-related items could be specific (e.g., rash) or global. While specific items are easier to describe and understand, their use may miss rarer symptoms and those that are currently unknown but will arise from future medical advancements. The appropriate number of additional items, the independence of those items, and their impact on the psychometric properties of the core instrument require consideration. Alternatively, a global item could encompass all potential treatment-related symptoms, of any treatments for any disease. However, such an item may not be well understood by general public respondents in valuation exercises. Further challenges include the decision about whether to generate de novo value sets for the modified instrument or to map to existing tariffs. The fluctuating and transient nature of treatment-related symptoms may be inconsistent with the methods used in conventional valuation exercises. Fluctuating symptoms could be missed by sub-optimal measure administration timing. The addition of items also poses double-counting risks. In summary, the addition of treatment-related symptom items could increase the sensitivity of existing utility measures to capture known and unknown treatment effects in oncology, while retaining the core domains. However, more research is needed to investigate the challenges, particularly regarding valuation.

Is the Management of Rectal Cancer Using a Watch and Wait Approach Feasible, Safe and Effective in a Publicly Funded General Hospital?

AIMS

Although there is emerging evidence to suggest equivalent oncological outcomes using a watch and wait approach compared with primary total mesorectal excision surgery, there is a paucity of evidence about the safety and efficacy of this approach in routine clinical practice. Here we report the long-term outcomes and quality of life from patients managed with watch and wait following a clinical complete response (cCR) to neoadjuvant therapy.

MATERIALS AND METHODS

Patients with adenocarcinoma of the rectum with cCR following neoadjuvant therapy managed using watch and wait were retrospectively identified. Demographic data, performance status, pretreatment staging information, oncological and surgical outcomes were obtained from routinely collected clinical data. Quality of life was measured by trained clinicians during telephone interviews.

RESULTS

Over a 7-year period, 506 patients were treated for rectal cancer, 276 had neoadjuvant therapy and 72 had a cCR (26.1%). Sixty-three were managed with watch and wait. Thirteen patients had mucosal regrowth. There was no significant difference in the incidence of metastatic disease between the surgical and watch and wait cohorts (P = 0.38). The 13 patients with mucosal regrowth underwent salvage surgery. Eleven of the patients who underwent surgical resection had R0 resections. There was also a statistically and clinically significant improvement in the Functional Assessment of Cancer Therapy - Colorectal (FACT-C) trial outcome index (P = 0.022).

CONCLUSION

This study shows that watch and wait is safe and effective outside of tertiary referral centres. It suggests that an opportunistic cCR is durable and when mucosal regrowth occurs it can be salvaged. Finally, we have shown that quality of life is probably improved if a watch and wait approach is adopted.

Current Status of Research on the Mapping Function of Health Utility Values in the Asia Pacific Region: A Systematic Review

OBJECTIVES

This systematic review aimed to analyze the published studies on the use of the mapping method between generic scales and disease-specific scales as well as between 2 universal scales.

METHODS

A systematic literature search was conducted using PubMed, ScienceDirect, Web of Science, CNKI, Weipa Database, Wanfang Database, and HERC Database to collect articles about the application of the mapping method to the measurement of health utility value from January 2000 to December 2019.

RESULTS

Overall, 59 articles met the inclusion requirements, and most of them were a mapping study between a disease-specific scale and a generic scale. Then all these articles were classified by the following study types: a clear functional relationship; unclear functional relationship; disease-specific scale and universality; mapping between generic scales and disease-specific scales, and mapping between universal scales. Most studies derived the best mapping model from the ordinary least squares regression, and fewer studies chose to use new regression methods. Sample sizes in the retrieved studies generally affected the reliability of the study results.

CONCLUSIONS

In recent years, as more attention has been paid to the research of the mapping method, a large number of problems have followed, such as the selection of scale types, the coverage of the study sample, and the selection of evaluation index of model performance and sample size. It is hoped that these problems can be properly solved in the future research.

Mapping the Kansas City Cardiomyopathy Questionnaire (KCCQ) Onto EQ-5D-3L in Heart Failure Patients: Results for the Japanese and UK Value Sets

Background. Health technology assessment bodies in several countries, including Japan and the United Kingdom, recommend mapping techniques to obtain utility scores in clinical trials that do not have a preference-based measure of health. This study sought to develop mapping algorithms to predict EQ-5D-3L scores from the Kansas City Cardiomyopathy Questionnaire (KCCQ) in patients with heart failure (HF). Methods. Data from the randomized, double-blind PARADIGM-HF trial were analyzed, and EQ-5D-3L scores were calculated using the Japanese and UK value sets. Several different model specifications were explored to best fit EQ-5D data collected at baseline with KCCQ scores, including ordinary least square regression, two-part, Tobit, and three-part models. Generalized estimating equations models were also fitted to analyze longitudinal EQ-5D data. To validate model predictions, the data set was split into a derivation (n = 4,465) from which the models were developed and a separate sample (n = 1,892) for validation. Results. There were only small differences between the different model classes tested. Model performance and predictive power was better for the item-level models than for the models including KCCQ domain scores. R ² statistics for the item-level models ranged from 0.45 to 0.52. Mean absolute error in the validation sample was 0.10 for the models using the Japanese value set and 0.114 for the UK models. All models showed some underprediction of utility above 0.75 and overprediction of utility below 0.5, but performed well for population-level estimates. Conclusions. Using data from a large clinical trial in HF, we found that EQ-5D-3L scores can be estimated from responses to the KCCQ and can facilitate cost-utility analysis from existing HF trials where only the KCCQ was administered. Future validation in other HF populations is warranted.

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 FACT-G onto EQ-5D-5L index for patients with cancer

Background

To develop direct and indirect (response) mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy General (FACT-G) onto the EQ-5D-5L index.

Methods

We conducted the QOL-MAC study where EQ-5D-5L, EORTC QLQ-C30, and FACT-G were cross-sectionally evaluated in patients receiving drug treatment for solid tumors in Japan. We developed direct and indirect mapping algorithms using 7 regression methods. Direct mapping was based on the Japanese value set. We evaluated the predictive performances based on root mean squared error (RMSE), mean absolute error, and correlation between the observed and predicted EQ-5D-5L indexes.

Results

Based on data from 903 and 908 patients for EORTC QLQ-C30 and FACT-G, respectively, we recommend two-part beta regression for direct mapping and ordinal logistic regression for indirect mapping for both EORTC QLQ-C30 and FACT-G. Cross-validated RMSE were 0.101 in the two methods for EORTC QLQ-C30, whereas they were 0.121 in two-part beta regression and 0.120 in ordinal logistic regression for FACT-G. The mean EQ-5D-5L index and cumulative distribution function simulated from the recommended mapping algorithms generally matched with the observed ones except for very good health (both source measures) and poor health (only FACT-G).

Conclusions

The developed mapping algorithms can be used to generate the EQ-5D-5L index from EORTC QLQ-C30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms.

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 clinical outcomes to generic preference-based outcome measures: development and comparison of methods

BACKGROUND

Cost-effectiveness analysis using quality-adjusted life-years as the measure of health benefit is commonly used to aid decision-makers. Clinical studies often do not include preference-based measures that allow the calculation of quality-adjusted life-years, or the data are insufficient. 'Mapping' can bridge this evidence gap; it entails estimating the relationship between outcomes measured in clinical studies and the required preference-based measures using a different data set. However, many methods for mapping yield biased results, distorting cost-effectiveness estimates.

OBJECTIVES

Develop existing and new methods for mapping; test their performance in case studies spanning different preference-based measures; and develop methods for mapping between preference-based measures.

DATA SOURCES

Fifteen data sets for mapping from non-preference-based measures to preference-based measures for patients with head injury, breast cancer, asthma, heart disease, knee surgery and varicose veins were used. Four preference-based measures were covered: the EuroQoL-5 Dimensions, three-level version (n = 11), EuroQoL-5 Dimensions, five-level version (n = 2), Short Form questionnaire-6 Dimensions (n = 1) and Health Utility Index Mark 3 (n = 1). Sample sizes ranged from 852 to 136,327. For mapping between generic preference-based measures, data from FORWARD, the National Databank for Rheumatic Diseases (which includes the EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, in its 2011 wave), were used.

MAIN METHODS DEVELOPED

Mixture-model-based approaches for direct mapping, in which the dependent variable is the health utility value, including adaptations of methods developed to model the EuroQoL-5 Dimensions, three-level version, and beta regression mixtures, were developed, as were indirect methods, in which responses to the descriptive systems are modelled, for consistent multidirectional mapping between preference-based measures. A highly flexible approach was designed, using copulas to specify the bivariate distribution of each pair of EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, responses.

RESULTS

A range of criteria for assessing model performance is proposed. Theoretically, linear regression is inappropriate for mapping. Case studies confirm this. Flexible, direct mapping methods, based on different variants of mixture models with appropriate underlying distributions, perform very well for all preference-based measures. The precise form is important. Case studies show that a minimum of three components are required. Covariates representing disease severity are required as predictors of component membership. Beta-based mixtures perform similarly to the bespoke mixture approaches but necessitate detailed consideration of the number and location of probability masses. The flexible, bi-directional indirect approach performs well for testing differences between preference-based measures.

LIMITATIONS

Case studies drew heavily on EuroQoL-5 Dimensions. Indirect methods could not be undertaken for several case studies because of a lack of coverage. These methods will often be unfeasible for preference-based measures with complex descriptive systems.

CONCLUSIONS

Mapping requires appropriate methods to yield reliable results. Evidence shows that widely used methods such as linear regression are inappropriate. More flexible methods developed specifically for mapping show that close-fitting results can be achieved. Approaches based on mixture models are appropriate for all preference-based measures. Some features are universally required (such as the minimum number of components) but others must be assessed on a case-by-case basis (such as the location and number of probability mass points).

FUTURE RESEARCH PRIORITIES

Further research is recommended on (1) the use of the monotonicity concept, (2) the mismatch of trial and mapping distributions and measurement error and (3) the development of indirect methods drawing on methods developed for mapping between preference-based measures.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 34. See the NIHR Journals Library website for further project information. This project was also funded by a Medical Research Council grant (MR/L022575/1).

The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

BACKGROUND

Mapping algorithms can be used to generate health state utilities when a preference-based instrument is not included in a clinical study. Our aim was to investigate the external validity of published mapping algorithms in non-small cell lung cancer (NSCLC) between the EORTC QLQ-C30 and EQ-5D instruments and to propose methodology for validating any mapping algorithms.

METHODS

We conducted a targeted literature review to identify published mappings, then applied these to data from the osimertinib clinical trial programme. Performance of the algorithms was evaluated using the mean absolute error, root mean squared error, and graphical techniques for the observed versus predicted EQ-5D utilities. These statistics were also calculated across the range of utility values (as well as ordinary least squares and quantile regression), to investigate how the mappings fitted across all values, not simply around the mean utility.

RESULTS

Three algorithms developed in NSCLC were identified. The algorithm based on response mapping (Young et al., 2015) fitted the validation dataset across the range of observed values with similar fit statistics to the original publication (overall MAE of 0.087 vs 0.134). The two algorithms based on beta-binomial models presented a poor fit to both the mean and distribution of utility values (MAE 0.176, 0.178).

CONCLUSIONS

The validation of mapping algorithms is key to demonstrating their generalisability beyond the original dataset, particularly across the range of plausible utility values (not just the mean) - perceived patient similarity being insufficient. The identified algorithm from Young et al. performed well across the range of EORTC scores observed, and thus appears most suitable for use in other studies of NSCLC patients.

Cost-effectiveness of the Collaborative Care to Preserve Performance in Cancer (COPE) trial tele-rehabilitation interventions for patients with advanced cancers

Purpose

The purpose of this analysis was to determine the cost‐effectiveness of a Collaborative Care Model (CCM)‐based, centralized telecare approach to delivering rehabilitation services to late‐stage cancer patients experiencing functional limitations.

Methods

Data for this analysis came from the Collaborative Care to Preserve Performance in Cancer (COPE) trial, a randomized control trial of 516 patients assigned to: (a) a control group (arm A), (b) tele‐rehabilitation (arm B), and (c) tele‐rehabilitation plus pharmacological pain management (arm C). Patient quality of life was measured using the EQ‐5D‐3L at baseline, 3‐month, and 6‐month follow‐up. Direct intervention costs were measured from the experience of the trial. Participants’ hospitalization data were obtained from their medical records, and costs associated with these encounters were estimated from unit cost data and hospital‐associated utilization information found in the literature. A secondary analysis of total utilization costs was conducted for the subset of COPE trial patients for whom comprehensive cost capture was possible.

Results

In the intervention‐only model, tele‐rehabilitation (arm B) was found to be the dominant strategy, with an incremental cost‐effectiveness ratio (ICER) of $15 494/QALY. At the $100 000 willingness‐to‐pay threshold, this tele‐rehabilitation was the cost‐effective strategy in 95.4% of simulations. It was found to be cost saving compared to enhanced usual care once the downstream hospitalization costs were taken into account. In the total cost analysis, total inpatient hospitalization costs were significantly lower in both tele‐rehabilitation (arm B) and tele‐rehabilitation plus pain management (arm C) compared to control (arm A), (P = .048).

Conclusion

The delivery of a CCM‐based, centralized tele‐rehabilitation intervention to patients with advanced stage cancer is highly cost‐effective. Clinicians and care teams working with this vulnerable population should consider incorporating such interventions into their patient care plans.

Effect of Collaborative Telerehabilitation on Functional Impairment and Pain Among Patients With Advanced-Stage Cancer: A Randomized Clinical Trial

Importance

Most patients with advanced-stage cancer develop impairment and pain-driven functional losses that jeopardize their independence.

Objective

To determine whether collaborative telerehabilitation and pharmacological pain management improve function, lessen pain, and reduce requirements for inpatient care.

Design, Setting, and Patients

The Collaborative Care to Preserve Performance in Cancer (COPE) study was a 3-arm randomized clinical trial conducted at 3 academic medical centers within 1 health care system. Patient recruitment began in March 2013 and follow-up concluded in October 2016. Participants (N = 516) were low-level community or household ambulators with stage IIIC or IV solid or hematologic cancer.

Interventions

Participants were randomly assigned to the (1) control arm, (2) telerehabilitation arm, or (3) telerehabilitation with pharmacological pain management arm. All patients underwent automated function and pain monitoring with data reporting to their care teams. Participants in arms 2 and 3 received 6 months of centralized telerehabilitation provided by a physical therapist-physician team. Those in arm 3 also received nurse-coordinated pharmacological pain management.

Main Outcomes and Measures

Blinded assessment of function using the Activity Measure for Postacute Care computer adaptive test, pain interference and average intensity using the Brief Pain Inventory, and quality of life using the EQ-5D-3L was performed at baseline and months 3 and 6. Hospitalizations and discharges to postacute care facilities were recorded.

Results

The study included 516 participants (257 women and 259 men; mean [SD] age, 65.6 [11.1] years), with 172 randomized to 1 of 3 arms. Compared with the control group, the telerehabilitation arm 2 had improved function (difference, 1.3; 95% CI, 0.08-2.35; P = .03) and quality of life (difference, 0.04; 95% CI, 0.004-0.071; P = .01), while both telerehabilitation arms 2 and 3 had reduced pain interference (arm 2, -0.4; 95% CI, -0.78 to -0.09; P = .01 and arm 3, -0.4; 95% CI, -0.79 to -0.10; P = .01), and average intensity (arm 2, -0.4; 95% CI, -0.78 to -0.07; P = .02 and arm 3, -0.5; 95% CI, -0.84 to -0.11; P = .006). Telerehabilitation was associated with higher odds of home discharge in arms 2 (odds ratio [OR], 4.3; 95% CI, 1.3-14.3; P = .02) and 3 (OR, 3.8; 95% CI, 1.1-12.4; P = .03) and fewer days in the hospital in arm 2 (difference, -3.9 days; 95% CI, -2.4 to -4.6; P = .01).

Conclusions and Relevance

Collaborative telerehabilitation modestly improved function and pain, while decreasing hospital length of stay and the requirement for postacute care, but these outcomes were not enhanced with the addition of pharmacological pain management.

Trial Registration

ClinicalTrials.gov identifier: NCT01721343.

Mapping function from FACT-B to EQ-5D-5 L using multiple modelling approaches: data from breast cancer patients in China

Background

The Functional Assessment of Cancer Therapy-Breast (FACT-B) is the most commonly used scale for assessing quality of life in patients with breast cancer. The lack of preference-based measures limits the cost-utility of breast cancer in China. The goal of this study was to explore whether a mapping function can be established from the FACT-B to the EQ-5D-5 L when the EQ-5D health-utility index is not available.

Methods

A cross-sectional survey of adults with breast cancer was conducted in China. All patients included in the study completed the EQ-5D-5 L and the disease-specific FACT-B questionnaire, and demographic and clinical data were also collected. The Chinese tariff value was used to calculate the EQ-5D-5 L utility scores. Five models were evaluated using three different modelling approaches: the ordinary least squares (OLS) model, the Tobit model and the two-part model (TPM). Total scores, domain scores, squared terms and interaction terms were introduced into models. The goodness of fit, signs of the estimated coefficients, and normality of prediction errors of the model were also assessed. The normality of the prediction error is determined by calculating the root mean squared error (RMSE), the mean absolute deviation (MAD), and the mean absolute error (MAE). Akaike information criteria (AIC) and Bayes information criteria (BIC) were also used to assess models and predictive performances. The OLS model was followed by simple linear equating to avoid regression to the mean.

Results

The performance of the models was improved after the introduction of the squared terms and the interaction terms. The OLS model, including the squared terms and the interaction terms, performed best for mapping the EQ-5D-5 L. The explanatory power of the OLS model was 70.0%. The AIC and BIC of this model were the smallest (AIC = -705.106, BIC = -643.601). The RMSE, MAD and MAE of the OLS model, Tobit model and TPM were similar. The MAE values of the 5-fold cross-validation of the multiple models in this study were 0.07155~0.08509; meanwhile, the MAE of the TPM was the smallest, followed by that of the OLS model. The OLS regression proved to be the most accurate for the mean, and linearly equated scores were much closer to observed scores.

Conclusions

This study establishes a mapping algorithm based on the Chinese population to estimate the EQ-5D-5 L index of the FACT-B and confirms that OLS models have higher explanatory power and that TPMs have lower prediction error. Given the accuracy of the mean prediction and the simplicity of the model, we recommend using the OLS model. The algorithm can be used to calculate EQ-5D scores when EQ-5D data are not directly collected in a study.

Health-related quality of life among patients with hypertension: population-based survey using EQ-5D-5L in Hong Kong SAR, China

OBJECTIVES

To evaluate the effect of hypertension and related comorbidities on the health-related quality of life (HRQoL) using EuroQol 5-dimensions instrument with five-level scale (EQ-5D-5L) Hong Kong (HK) version.

DESIGN

Data were analysed by a secondary data analysis based on a cross-sectional study assessing experience on public specialist outpatient service.

SETTING

A representative sample was recruited from 26 specialist outpatient clinics in HK.

PARTICIPANTS

A total of 4528 patients with hypertension aged 18 or above who responded to the survey.

INTERVENTION

EQ-5D-5L HK was applied to assess the HRQoL. The five-dimension descriptive system and the utility index of EQ-5D-5L were treated as the dependent variable in the current study. Regression modelling was applied to estimate the effect of hypertension and related comorbidities on health-related quality of life.

RESULTS

More respondents were women (53.9%), aged ≥65 years old (60.1%), and with primary educational attainment or below (52.3%). A total of 1466 respondents (32.4%) also reported suffering from diabetes, heart disease (20.8%), vision problem (1.7%) and cancer (1.5%). In the ordinal least squares model, utility decreased most when patients reported having physical disability associated with hypertension (beta=-0.395, SE=0.047), and 0.128, 0.064, 0.05 and 0.048 for mental problem, cancer, vision problem and heart problem. In the Tobit model, the utility reduced most for comorbidity of physical disability, and then mental problem, cancer, vision problem, heart problem and diabetes. For first part of two-part model, respondents coliving with mental problem were 10% less likely to report a full health. For the second part of two-part model, the respondents with physical disability had 0.294 lower in utility.

CONCLUSIONS

Respondents with hypertension reported a low EQ-5D utility score. Respondents were women, with a high education, fully employed and living with families reported better HRQoL. There is a significant effect of comorbidity influence on the decreased HRQoL, particularly those with physical disability and mental problem.

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 the FACT-B Instrument to EQ-5D-3L in Patients with Breast Cancer Using Adjusted Limited Dependent Variable Mixture Models versus Response Mapping

BACKGROUND

Preference-based measures of health, such as the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), are required to calculate quality-adjusted life-years for use in cost-effectiveness analysis, but are often not recorded in clinical studies. In these cases, mapping can be used to estimate preference-based measures.

OBJECTIVES

To model the relationship between the EQ-5D-3L and the Functional Assessment of Cancer Therapy-Breast Cancer (FACT-B) instrument, comparing indirect and direct mapping methods, and the use of FACT-B summary score versus FACT-B subscale scores.

METHODS

We used data from three clinical studies for advanced breast cancer providing 11,958 observations with full information on FACT-B and the EQ-5D-3L. We compared direct mapping using adjusted limited dependent variable mixture models (ALDVMMs) with indirect mapping using seemingly unrelated ordered probit models. The EQ-5D-3L was estimated as a function of FACT-B and other patient-related covariates.

RESULTS

The use of FACT-B subscale scores was better than using the total FACT-B score. A good fit to the observed data was observed across the entire range of disease severity in all models. ALDVMMs outperformed the indirect mapping. The breast cancer-specific scale had a strong influence in predicting the pain/discomfort and self-care dimensions of the EQ-5D-3L.

CONCLUSIONS

This article adds to the growing literature that demonstrates the performance of the ALDVMM method for mapping. Regardless of which model is used, the subscales of FACT-B should be included as independent variables wherever possible. The breast cancer-specific subscale of FACT-B is important in predicting the EQ-5D-3L. This suggests that generic cancer measures should not be used for utility mapping in patients with breast cancer.

Does Selecting Covariates Using Factor Analysis in Mapping Algorithms Improve Predictive Accuracy? A Case of Predicting EQ-5D-5L and SF-6D Utilities from the Women's Health Questionnaire

BACKGROUND

In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping nonutility measures onto utilities. However, it is not clear whether using exploratory factor analysis (EFA) as one such method improves the predictive ability of these algorithms.

OBJECTIVE

This question is addressed within the context of mapping a non-utility-based outcome, the core 23-item Women's Health Questionnaire (WHQ-23), onto two utility instruments: five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) and the six-dimensional health state short form (derived from short form 36 health survey) (SF-6D).

METHODS

Data on all three outcomes were collected from 455 women from the Australian general population participating in a study assessing attitudes toward in vitro fertilization. Statistical methods for selecting covariates included stepwise regression (SW), including all covariates (Include all), multivariable fractional polynomial (MFP), and EFA. The predictive accuracy of 108 regression models was assessed using five criteria: mean absolute error, root mean squared error, correlation, distribution of predicted utilities, and proportion of predictions with absolute errors of less than 0.0.5. Validation of "primary" models was carried out on random samples of the in vitro fertilization study.

RESULTS

The best results for EQ-5D-5L and SF-6D predictions were obtained from models using SW, "Include all," and MFP covariate-selection approaches. Root mean squared error (0.0762-0.1434) and mean absolute error (0.0590-0.0924) estimates for these models were within the range of published estimates. EFA was outperformed by other covariate-selection methods.

CONCLUSIONS

It is possible to predict valid utilities from the WHQ-23 using regression methods based on SW, "Include all," and MFP covariate-selection techniques.

Development of Methods for the Mapping of Utilities Using Mixture Models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in Patients with Asthma

BACKGROUND

Studies have shown that methods based on mixture models work well when mapping clinical to preference-based methods.

OBJECTIVES

To develop these methods in different ways and to compare performance in a case study.

METHODS

Data from 856 patients with asthma allowed mapping between the Asthma Quality of Life Questionnaire and both the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) and the health utilities index mark 3 (HUI3). Adjusted limited dependent variable mixture models and beta-based mixture models were estimated. Optional inclusion of the gap between full health and the next value as well as a mass point at the next feasible value were explored.

RESULTS

In all cases, model specifications formally modeling the gap between full health and the next feasible value were an improvement on those that did not. Mapping to the HUI3 required more components in the mixture models than did mapping to the EQ-5D-5L because of its uneven distribution. The optimal beta-based mixture models mapping to the HUI3 included a probability mass at the utility value adjacent to full health. This is not the case when estimating the EQ-5D-5L, because of the low proportion of observations at this point.

CONCLUSIONS

Beta-based mixture models marginally outperformed adjusted limited dependent variable mixture models with the same number of components in this data set. Nevertheless, they require a larger number of parameters and longer estimation time. Both mixture model types closely fit both EQ-5D-5L and HUI data. Standard mapping approaches typically lead to biased estimates of health gain. The mixture model approaches exhibit no such bias. Both can be used with confidence in applied cost-effectiveness studies. Future mapping studies in other disease areas should consider similar methods.

Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP

Background

Economic evaluation normally requires information to be collected on outcome improvement using utility values. This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult. One potential solution is the use of mapping to generate utility values from clinical measures. This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D (EQ-5D-5 L) and the ICEpop CAPability measure for Adults (ICECAP-A) from the three commonly used clinical measures; the CORE-OM, the LDQ and the TOP measures.

Methods

Models were estimated using pilot trial data of heroin users in opiate substitution treatment. In the trial the EQ-5D-5 L, ICECAP-A, CORE-OM, LDQ and TOP were administered at baseline, three and twelve month time intervals. Mapping was conducted using estimation and validation datasets. The normal estimation dataset, which comprised of baseline sample data, used ordinary least squares (OLS) and tobit regression methods. Data from the baseline and three month time periods were combined to create a pooled estimation dataset. Cluster and mixed regression methods were used to map from this dataset. Predictive accuracy of the models was assessed using the root mean square error (RMSE) and the mean absolute error (MAE). Algorithms were validated using sample data from the follow-up time periods.

Results

Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence. Although both measures can be predicted, the ICECAP-A was better predicted by the clinical measures. There were no advantages of pooling the data. There were 6 chosen mapping algorithms, which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178.

Conclusion

It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping. In the context of opiate dependence, these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options.

Trial registration

ISRCTN22608399. Date of registration: 27/04/2012. Date of first randomisation: 14/08/2012.

Electronic supplementary material

The online version of this article (10.1186/s12955-018-0926-7) contains supplementary material, which is available to authorized users.

Cost-effectiveness of everolimus for the treatment of advanced neuroendocrine tumours of gastrointestinal or lung origin in Canada

BACKGROUND

In 2016, everolimus was approved by Health Canada for the treatment of unresectable, locally advanced or metastatic, well-differentiated, non-functional, neuroendocrine tumours (NET) of gastrointestinal (GI) or lung origin in adult patients with progressive disease. This analysis evaluated the cost-effectiveness of everolimus in this setting from a Canadian societal perspective.

METHODS

A partitioned survival model was developed to compare the cost per life-year (LY) gained and cost per quality-adjusted life-year (QALY) gained of everolimus plus best supportive care (BSC) versus BSC alone in patients with advanced or metastatic NET of GI or lung origin. Model health states included stable disease, disease progression, and death. Efficacy inputs were based on the RADIANT-4 trial and utilities were mapped from quality-of-life data retrieved from RADIANT-4. Resource utilization inputs were derived from a Canadian physician survey, while cost inputs were obtained from official reimbursement lists from Ontario and other published sources. Costs and efficacy outcomes were discounted 5% annually over a 10-year time horizon, and sensitivity analyses were conducted to test the robustness of the base case results.

RESULTS

Everolimus had an incremental gain of 0.616 QALYs (0.823 LYs) and CA$89,795 resulting in an incremental cost-effectiveness ratio of CA$145,670 per QALY gained (CA$109,166 per LY gained). The probability of cost-effectiveness was 52.1% at a willingness to pay (WTP) threshold of CA$150,000 per QALY.

CONCLUSIONS

Results of the probabilistic sensitivity analysis indicate that everolimus has a 52.1% probability of being cost-effective at a WTP threshold of CA$150,000 per QALY gained in Canada.

Does the EQ-5D capture the concerns measured by the Palliative care Outcome Scale? Mapping the Palliative care Outcome Scale onto the EQ-5D using statistical methods

BACKGROUND

The main measure to generate utility data for economic evaluations is the EQ-5D, but no study has tested whether or how to map from palliative care measures to the EQ-5D.

AIMS

To assess the level of conceptual overlap between palliative outcomes and the EQ-5D, and the feasibility of mapping between them to obtain utilities for the Palliative care Outcome Scale.

DESIGN

A cross-sectional secondary analysis of data from three studies.

SETTING/PARTICIPANTS

Patients receiving palliative care and bereaved relatives, recruited from three tertiary National Health Service hospitals in South London.

METHODS

The overlap between both measures was assessed using principal component analysis. The Palliative care Outcome Scale was mapped onto the EQ-5D using three regression models.

RESULTS

Spearman's correlations between both instruments were low (mean rho = 0.11). The principal component analysis showed the Palliative care Outcome Scale is associated with only two EQ-5D dimensions (pain; and anxiety/depression). No Palliative care Outcome Scale items loaded onto the mobility, self-care and usual activities dimensions of the EQ-5D. The mapping models performed poorly at predicting utilities from Palliative care Outcome Scale data (mean absolute error >0.3 and R² <0.10). Hence, none of the models can be recommended as acceptable for calculating utilities from Palliative care Outcome Scale responses.

CONCLUSION

Differences between the Palliative care Outcome Scale and the EQ-5D do not undermine the qualities of either instrument when used for their own purposes. However, due to conceptual differences, the EQ-5D does not capture some of the concerns measured by the Palliative care Outcome Scale, and therefore, mapping onto the EQ-5D is unlikely to provide an appropriate basis for estimating utilities for conducting economic evaluations in palliative care studies.

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

Background

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

Methods

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

Results

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

Conclusions

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