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

Mapping the ADDQoL to the EQ-5D-5L and SF-6Dv2 among Chinese patients with type 2 diabetes mellitus

OBJECTIVE

The Audit of Diabetes-Dependent Quality of Life (ADDQoL) is a widely used instrument for assessing quality of life in Type 2 Diabetes Mellitus (T2DM). However, it does not directly yield health utility values essential for economic evaluations. This study developed mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores in T2DM patients in China.

METHODS

Cross-sectional data from 800 T2DM patients in China, stratified by age, sex, and geographical region, were divided into development (80%) and validation (20%) groups. Pearson correlation analyses were conducted to assess the conceptual overlap between ADDQoL and the EQ-5D-5L and SF-6Dv2. Six predictor sets and six regression methods were explored to map ADDQoL scores to EQ-5D-5L and SF-6Dv2 utility values, respectively. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), and intraclass correlation coefficient (ICC).

RESULTS

For the development group, the mean (SD) ADDQoL Average Weighted Impact (AWI) score was - 2.426 (1.052), and the mean (SD) utility values for EQ-5D-5L and SF-6Dv2 were 0.928 (0.092) and 0.791 (0.133), respectively. Among all 36 alternative mapping models each for EQ-5D-5L and SF-6Dv2, the best performance was consistently observed in the two-part models that included the ADDQoL AWI, the first overview item, and their squared terms. For the algorithm mapping to EQ-5D-5L utility values, it achieved a MAE of 0.067, a RMSE of 0.095, and an ICC of 0.414; For the algorithm mapping to SF-6Dv2 utility values, the corresponding metrics were an MAE of 0.099, an RMSE of 0.120, and an ICC of 0.517.

CONCLUSIONS

This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores. These algorithms could be used to support economic evaluations, specifically tailored for Chinese T2DM populations.

The EORTC QLQ breast modules and the FACT-B for assessing quality of life in breast cancer patients - an updated literature review

PURPOSE OF REVIEW

Two commonly used quality of life questionnaires in breast cancer are EORTC QLQ-BR23, the FACT-B, and the extended FACT-B + 4. More recently, the EORTC EORTC QLQ-BR42 was developed. This systematic review compares the various versions of the EORTC QLQ and FACT tools for breast cancer in terms of their content, validity, and psychometric properties.

RECENT FINDINGS

Thirty-six studies met the inclusion criteria. All questionnaires have been proven to be valid, reliable and responsive. The provisional EORTC QLQ-BR45 transitioned to the EORTC QLQ-BR42 in Phase IV of its development, which encompasses the side effects associated with the latest breast cancer treatments. Both the EORTC and FACT measures assess physical and mental dimensions of quality of life, with the EORTC measure placing relatively more emphasis on physical content and FACT placing relatively more emphasis on mental (social and emotional) content. The four additional items in the FACT-B + 4 were developed to address arm lymphoedema following axillary surgery.

SUMMARY

The development and uptake of quality of life tools are essential in the evaluation of breast cancer treatments. The EORTC QLQ-BR42 and FACT-B are both valid, reliable, and responsive QoL questionnaires.

Developing mapping algorithms to predict EQ-5D health utility values from Bath Ankylosing Spondylitis Disease Activity Index and Bath Ankylosing Spondylitis Functional Index among patients with Ankylosing Spondylitis

BACKGROUND

Preference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).

METHODS

Patients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman's correlation coefficient and concordance correlation coefficient were used to access predictive performance.

RESULTS

The 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did.

CONCLUSION

This study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.

Mapping the EORTC QLQ-C30 and QLQ H&N35 to the EQ-5D-5L and SF-6D for papillary thyroid carcinoma

PURPOSE

Empirical evidence for the EORTC QLQ C30 scale in thyroid cancer mapping algorithms has not been found in China, which limits the cost-utility analysis of patients with papillary thyroid carcinoma (PTC) population. We developed mapping algorithms that use the EORTC QLQ-C30 and QLQ H&N35 to predict EQ-5D-5L and SF-6D health utility scores for PTC patients.

METHODS

Data from 1050 Chinese PTC patients who completed the EORTC QLQ-C30, QLQ H&N35, EQ-5D-5L and SF-6D instruments were collected. Direct mapping (OLS, Tobit, Betamix) and indirect mapping functions (Order Probit) were used to estimate algorithms. The goodness-of-fit of mapping performance was assessed by MAE, RMSE, AIC, BIC, AE, and ICC. A fivefold cross-validation and random sample validation approach were used to test the stability of the models.

RESULTS

The mean EQ-5D-5L and SF-6D utility scores were 0.8704 and 0.6368, respectively. We recommend the Betamix model for the EQ-5D-5L (MAE = 0.0363, RMSE = 0.0505, AIC = -3458.73, BIC = -3096.91, AE > 0.05(%) = 48.38, AE > 0.1(%) = 8.67, ICC = 0.8288 for the full sample dataset) and the Betamix model for the SF-6D (MAE = 0.0328, RMSE = 0.0417, AIC = -2788.91, BIC = -2605.51, AE > 0.05(%) = 42.76, AE > 0.1(%) = 3.62, ICC = 0.8657 for the full sample dataset), with EORTC QLQ-C30 all items, QLQ H&N35 all items, age and gender as the predicted variables showing the best performance.

CONCLUSION

In the absence of preference-based quality of life tools, the mapping algorithms reported here are effective alternative for predicting the health utility of PTC patients, contributing to the cost-utility analysis studies.

Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities

BACKGROUND

The Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure.

METHODS

Service users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range.

RESULTS

Analyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior.

CONCLUSIONS

This study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

Prediction of the SF-6D utility score from Lung cancer FACT-L: a mapping study in China

OBJECTIVE

To develop a mapping algorithm for generating the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients.

METHODS

Data were collected from 625 lung cancer patients in mainland China. The Spearman rank correlation coefficient and principal component analysis were used to evaluate the conceptual overlap between the FACT-L and SF-6D. Five model specifications and four statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit and beta-mixture regression models, which were used to directly estimate health utility, and ordered probit regression was used to predict the response level. The prediction performance was evaluated using the correlations between the root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the observed and predicted SF-6D scores. A five-fold cross-validation method was used to test the universality of each model and select the best model.

RESULTS

The average FACT-L score was 103.024. The average SF-6D score was 0.774. A strong correlation was found between FACT-L and SF-6D scores (ρ = 0.797). The ordered probit regression model with the total score of each dimension and its square term, as well as age and sex as covariates, was most suitable for mapping FACT-L to SF-6D scores (5-fold cross-validation: RMSE = 0.0854; MAE = 0.0655; CCC = 0.8197; AEs > 0.1 (%) = 53.44; AEs > 0.05 (%) = 21.76), followed by beta-mixture regression for direct mapping. The Bland‒Altman plots showed that the ordered probit regression M5 had the lowest proportion of prediction scores outside the 95% agreement limit (-0.166, 0.163) at 4.96%.

CONCLUSIONS

The algorithm reported in this paper enables lung cancer data from the FACT-L to be mapped to the utility of the SF-6D. The algorithm allows the calculation of quality-adjusted life years for cost-utility analyses of lung cancer.

Mapping the schizophrenia quality of life scale to EQ-5D, HUI3 and SF-6D utility scores in patients with schizophrenia

OBJECTIVES

The current study aimed to map the disease-specific Schizophrenia Quality of Life Scale (SQLS) onto the three- and five-level EuroQol five-dimension (EQ-5D-3 L and EQ-5D-5 L), Health Utility Index Mark 3 (HUI3) and Short Form six-dimensional (SF-6D) preference-based instruments to inform future cost-utility analyses for treatment of patients with schizophrenia.

METHODS

Data from 251 outpatients with schizophrenia spectrum disorders was included for analysis. Ordinary least square (OLS), Tobit and beta regression mixture models were employed to estimate the utility scores. Three regression models with a total of 66 specifications were determined by goodness of fit and predictive indices. Distribution of the original data to the distributions of the data generated using the preferred estimated models were then compared.

RESULTS

EQ-5D-3 L and EQ-5D-5 L were best predicted by the OLS model, including SQLS domain scores, domain-squared scores, age, and gender as explanatory predictors. The models produced the best performance index and resembled most closely with the observed EQ-5D data. HUI3 and SF-6D were best predicted by the OLS and Tobit model respectively.

CONCLUSION

The current study developed mapping models for converting SQLS scores into generic utility scores, which can be used for economic evaluation among patients with schizophrenia.

Mapping study of papillary thyroid carcinoma in China: Predicting EQ-5D-5L utility values from FACT-H&N

Objective

To develop a mapping algorithm that can be used to predict EQ-5D-5L health utility scores from FACT-H&N and obtain health utility parameters for Chinese patients with papillary thyroid carcinoma (PTC), which can be used for cost-utility analysis in health economic.

Methods

A total of 1,050 patients with PTC from a tertiary hospital in China were included, and they completed FACT-H&N and EQ-5D-5L. Four mapping algorithms of direct mapping functions were used to derive the models: Ordinary least squares (OLS), Tobit model (Tobit), Two-part model (TPM), and Beta mixture regression model (Beta). The goodness-of-fit of models was assessed by the mean absolute error (MAE), root mean square error (RMSE), Akaike information criteria (AIC), Bayesian information criteria (BIC), and absolute error (AE). A fivefold cross-validation method was used to test the stability of the models.

Results

The mean utility value of the EQ-5D-5L was 0.870 ± 0.094. The mean EQ-VAS score was 76.5 ± 13.0. The Beta mixture regression model mapping FACT-H&N to EQ-5D-5L achieved the best performance [fivefold cross-validation MAE = 0.04612, RMSE = 0.06829, AIC = -2480.538, BIC = -2381.137, AE > 0.05 (%) = 32.48, AE > 0.1 (%) = 8.95]. The independent variables in this model were Physical Well-Being (PWB), Emotional Well-Being (EWB), Head & Neck Cancer Subscale (HNCS) scores and its square term and interaction term scores.

Conclusions

This study calculated the health utility score of Chinese patients with PTC. The reported algorithms can be used to map the FACT-H&N into the EQ-5D-5L, which can be applied in the cost-utility related study of patients with PTC.

Mapping of disease-specific Oxford Knee Score onto EQ-5D-5L utility index in knee osteoarthritis

BACKGROUND

EQ5D is a generic measure of health. It provides a single index value for health status that can be used in the clinical and economic evaluation of healthcare. Oxford Knee Score (OKS) is a joint-specific outcome measure tool designed to assess symptoms and function in osteoarthritis patients after joint replacement surgery. Though widely used, it has the disadvantage of lacking health index value. To fill the gap between functional and generic questionnaires with economic value, we linked generic EQ-5D-5L to the specific OKS to give a single index value for health status in KOA patients.

QUESTIONS/PURPOSES

Developing and evaluating an algorithm to estimate EuroQoL generic health utility scores (EQ-5D-5L) from the disease-specific OKS using data from patients with knee osteoarthritis (KO).

PATIENTS AND METHODS

This is a cross-sectional study of 571 patients with KO. We used four distinct mapping algorithms: Cumulative Probability for Ordinal Data, Penalized Ordinal Regression, CART (Classification and Regression Trees), and Ordinal random forest. We compared the resultant models' degrees of accuracy.

RESULTS

Mobility was best predicted by penalized regression with pre-processed predictors, usual activities by random forest, pain/discomfort by cumulative probability with pre-processed predictors, self-care by random forest with RFE (recursive feature elimination) predictors, and anxiety/depression by CART with RFE predictors. Model accuracy was lowest with anxiety/depression and highest with mobility and usual activities. Using available country value sets, the average MAE was 0.098 ± 0.022, ranging from 0.063 to 0.142; and the average MSE was 0.020 ± 0.008 ranging from 0.008 to 0.042.

CONCLUSIONS

The current study derived accurate mapping techniques from OKS to the domains of EQ-5D-5L, allowing for the computation of QALYs in economic evaluations. A machine learning-based strategy offers a viable mapping alternative that merits further exploration.

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 health assessment questionnaire disability index onto EQ-5D-5L in China

Objective

This research aimed to develop the more accurate mapping algorithms from health assessment questionnaire disability index (HAQ-DI) onto EQ-5D-5L based on Chinese Rheumatoid Arthritis patients.

Methods

The cross-sectional data of Chinese RA patients from 8 tertiary hospitals across four provincial capitals was used for constructing the mapping algorithms. Direct mapping using Ordinary least squares regression (OLS), the general linear regression model (GLM), MM-estimator model (MM), Tobit regression model (Tobit), Beta regression model (Beta) and the adjusted limited dependent variable mixture model (ALDVMM) and response mapping using Multivariate Ordered Probit regression model (MV-Probit) were carried out. HAQ-DI score, age, gender, BMI, DAS28-ESR and PtAAP were included as the explanatory variables. The bootstrap was used for validation of mapping algorithms. The average ranking of mean absolute error (MAE), root mean square error (RMSE), adjusted R ² (adjR ²) and concordance correlation coefficient (CCC) were used to assess the predictive ability of the mapping algorithms.

Results

According to the average ranking of MAE, RMSE, adjR ², and CCC, the mapping algorithm based on Beta performed the best. The mapping algorithm would perform better as the number of variables increasing.

Conclusion

The mapping algorithms provided in this research can help researchers to obtain the health utility values more accurately. Researchers can choose the mapping algorithms under different combinations of variables based on the actual data.

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.

Mapping the Haem-A-QoL to the EQ-5D-5L in patients with hemophilia

OBJECTIVE

This study's objective was to develop an algorithm that mapping the Haem-A-QoL scores to EQ-5D-5L utility scores in patients with hemophilia in China.

METHODS

A national sample of 862 patients with hemophilia completed both the EQ-5D-5L and Haem-A-QoL instruments. Eight regression models were selected to develop the mapping algorithm, they were: the ordinary least squares, general linear regression, Tobit regression, censored least absolute deviation, mixture beta regression, adjusted limited dependent variable mixture, the two-part, and robust MM-estimator model. Root mean squared error (RMSE), mean absolute error (MAE), and R-square (R²) calculated using the tenfold cross-validation and random sample validation methods were used to assess the predictive ability of the models.

RESULTS

Based on RMSE, MAE, and R², the mixture beta regression model with selected Haem-A-QoL subscale scores as the predicted variables showed the best performance.

CONCLUSIONS

Our mapping algorithm bolsters the calculation of QALYs while conducting an economic evaluation of hemophilia-related interventions when only Haem-A-QoL data are available. The external validity of the algorithm should be further assessed in the other populations.

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.

Quality of Life and Hope of Women in China Receiving Chemotherapy for Breast Cancer

We explore the association of hope and quality of life in breast cancer chemotherapy women. Their quality of life is related to treatment effects and disease outcomes. This cross-sectional study was conducted in City, China, in 2017. In a convenience sampling, 450 women who underwent breast cancer chemotherapy were selected from two hospitals. Descriptive statistics, single-factor analysis, Spearman correlation, linear regression, and structural equation modeling were used to analyze data. The mean quality of life score was 65.65. In linear regression analysis, we found patients' quality of life was significantly related to age, marital status, education level, chemotherapy cycle, and hope. Structural equation results showed the "temporality and future" and "interconnectedness" subscales of the HHI explained 43% of the variance in quality of life. We found hope is an important aspect in quality of life, and further research is needed to determine if nurses can influence this aspect of care.

Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in patients with depression

BACKGROUND

There is limited evidence of mapping clinical instruments to a generic preference-based instrument in Asian patient populations. The current study aims to map the eight-item Patient Health Questionnaire depression scale (PHQ-8) onto the EuroQol Five-Dimension (EQ-5D), the Health Utilities Index Mark 3 (HUI3) and the Short Form Six-Dimension (SF-6D) which helps to inform future cost-utility analyses of treatments for depression.

METHODS

A total of 249 participants who had completed PHQ-8, EQ-5D, SF-6D and HUI3 questionnaires were included in the analyses. A beta regression mixture model was used to map the utility scores as a function of PHQ-8 total scores, PHQ-squared, age and gender. The predictive accuracy of the models was examined using mean absolute error and root mean square error.

RESULTS

The results were compared against two common regression methods including Ordinary Least Square (OLS) and Tobit regression models. The mean age of the sample was 36.2 years (SD = 11.1). The mean EQ-5D-3L, EQ-5D-5L, HUI3 and SF-6D utility scores were 0.615, 0.709, 0.461 and 0.607, respectively. The EQ-5D-3L, EQ-5D-5L and SF-6D utility scores were best predicted by the beta mixture regression model consisting of PHQ-8 total sores, PHQ-squared, and covariates including age and gender. The HUI3 was best predicted by the OLS regression model.

CONCLUSIONS

The current study provides important evidence to clinicians and researchers about the mapping algorithms that can be used in economic evaluation among patients with depression.

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.

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).

Mapping WOMAC Onto the EQ-5D-5L Utility Index in Patients With Hip or Knee Osteoarthritis

OBJECTIVES

To map the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) onto the EQ-5D-5L in patients with hip or knee osteoarthritis (OA).

METHODS

A prospective observational study was conducted on 758 patients with hip or knee OA who completed the EQ-5D-5L and WOMAC questionnaires, of whom 644 completed them both again 6 months later. Baseline data were used to derive mapping functions. Generalized additive models were used to identify to which powers the WOMAC subscales should be raised to achieve a linear relationship with the response. For the modeling, general linear models (GLM), Tobit models, and beta regression models were used. Age, sex, and affected joints were also considered. Preferred models were selected based on Akaike and Bayesian information criteria, adjusted R², mean absolute error (MAE), and root mean squared error (RMSE). The functions were validated with the follow-up data using MAE, RMSE, and the intraclass correlation coefficient.

RESULTS

The preferred models were a GLM with Pain²+Pain³+Function+Pain·Function as covariates and a beta model with Pain³+Function+Function²+Function³ as covariates. The adjusted R² were similar (0.6190 and 0.6136, respectively). The predictive performance of these models in the validation sample was similar and both models showed an overprediction for health states worse than death.

CONCLUSION

To our knowledge, these are the first functions mapping the WOMAC onto the EQ-5D-5L in patients with hip or knee OA. They showed an acceptable fit and precision and could be very useful for clinicians and researchers when cost-effectiveness studies are needed and generic preference-based health-related quality of life instruments to derive utilities are not available.

Mapping to Quality of Life and Capability Measures in Cataract Surgery Patients: From Cat-PROM5 to EQ-5D-3L, EQ-5D-5L, and ICECAP-O Using Mixture Modelling

Objectives. Cataract is a prevalent and potentially blinding eye condition. Cataract surgery, the only proven treatment for this condition, is a very frequently undertaken procedure. The objective of this analysis was to develop a mapping algorithm that could be used to predict quality of life and capability scores from the Cat-PROM5, a newly developed, validated patient-reported outcome measure for patients undergoing cataract surgery. Methods. We estimated linear models and adjusted limited dependent variable mixture models. Data were taken from the Predict-CAT cohort of up to 1181 patients undergoing cataract surgery at two sites in England. The Cat-PROM5 was mapped to two quality of life measures (EQ-5D-3L and EQ-5D-5L) and one capability measure (ICECAP-O). All patients reported ICECAP-O and one or other of the EQ-5D measures both before and after cataract surgery. Model performance was assessed using likelihood statistics, graphical inspections of model fit, and error measurements. Results. Adjusted limited dependent variable mixture models dominated linear models on all performance criteria. Mixture models offered very good fit. Three component models that allowed component membership to be a function of covariates (age, sex, and diabetic status depending on specification and outcome measure) and which conditioned on covariates offered the best performance in almost all cases. An exception was the EQ-5D-5L post-surgery for which a two-component model was selected. Conclusions. Mapping from Cat-PROM5 to quality of life and capability measures using adjusted limited dependent variable mixture models is feasible, and the estimates can be used to support cost-effectiveness analysis in relation to cataract care. Mixture models performed strongly for both quality of life outcomes and capability outcomes.

Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis

OBJECTIVES

To develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis.

METHODS

We used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation.

RESULTS

Mixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS.

CONCLUSIONS

This study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).

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.