The Use of Mapping to Estimate Health State Utility Values

Health state utility values in patients with Ankylosing Spondylitis: a systematic review and meta-analysis

INTRODUCTION

Ankylosing spondylitis (AS) is a chronic condition that requires lifelong treatment and results in a serious disease burden. Health state utility values (HSUVs) are a valuable tool for quantifying this burden and conducting cost-utility analysis.

OBJECTIVE

We conducted a systematic review and meta-analysis to obtain estimates of HSUVs in patients with AS, explored potential sources of heterogeneity, and compared pooled patient HSUVs with population norms.

METHOD

We searched PubMed, Embase, Web of science, Cochrane database and Scopus until July, 2023 to obtain eligible studies. The methodological quality of the included studies was assessed using the ROBINS-I checklist.

RESULTS

Forty-two publications involving 11,354 participants were included in this systematic review. The most commonly used instrument is the EQ-5D (38 studies). The estimated HSUVs for patients with AS from all available studies was pooled as 0.62 (95% CI 0.59 to 0.65). The pooled mean utility estimates from the random effects meta-analysis for SF-6D, EQ-5D-3L, EQ-5D-5L, and HUI3 were 0.65 (95% CI 0.62,0.68), 0.63 (95% CI 0.59,0.66), 0.60 (95% CI 0.42,0.79), and 0.48 (95% CI 0.43,0.53), respectively. For the EQ-5D-3L we conducted stratified meta-analyses and meta-regression based on key subgroups. The pooled estimates of EQ-5D-3L were lower for patients published before 2010, with high disease activity, long duration of disease, and in developed countries.

CONCLUSION

Pooled estimates of HSUVs for people with AS were substantially lower than population norms. These estimates provide robust evidence that can inform the economic evaluation of new therapies for individuals with AS.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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 Seattle Angina Questionnaire to EQ-5D-5L in patients with coronary heart disease

BACKGROUND

Health economic evaluation is critical in supporting novel cardiovascular disease therapies. However, most clinical studies do not include preference-based questionnaires to calculate utilities for health economic evaluations. Thus, this study aimed to develop mapping algorithms that convert the Seattle Angina Questionnaire (SAQ) to EQ-5D-5L health utility scores for patients with coronary health disease (CHD) in China.

METHODS

Data were obtained from a longitudinal study of patients with CHD conducted at the Tianjin Medical University General Hospital in China. Convenience sampling was used to recruit patients with CHD. The inclusion criteria were having been diagnosed with CHD through a medical examination and being aged 18 years or older. The exclusion criteria were a lack of comprehension ability, serious comorbidities, mental illness, and hearing or vision impairment. All eligible patients were invited to participate, and 305 and 75 patients participated at baseline and in the follow-up, respectively. Seven regression models were developed using a direct approach. Furthermore, we predicted the five EQ-5D items using ordered logit model and derived the utility score from predicted responses using an indirect approach. Model performances were evaluated using mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (ρ), and Lin's concordance correlation coefficient (CCC). A five-fold cross-validation method was used to evaluate internal validation.

RESULTS

The average age was 63.04 years, and 53.72% of the included patients were male. Most (70.05%) patients had unstable angina pectoris, and the mean illness duration was 2.50 years. The EQ-5D scores were highly correlated with five subscales of the SAQ, with Spearman's rank correlation coefficients ranging from 0.6184 to 0.7093. The mixture beta model outperformed the other regression models in the direct approach, with the lowest MAE and RMSE and highest ρ and CCC. The ordered logit model in the indirect approach performed the same as the mixture beta regression with equal MAE, lower RMSE, and higher ρ and CCC.

CONCLUSION

Mapping algorithms developed using mixture beta and ordered logit models accurately converted SAQ scores to EQ-5D-5L health utility values, which could support health economic evaluations related to coronary heart disease.

Generating EQ-5D-5L health utility scores from BASDAI and BASFI: a mapping study in patients with axial spondyloarthritis using longitudinal UK registry data

BACKGROUND

Preference-based health-state utility values (HSUVs), such as the EuroQol five-dimensional questionnaire (EQ-5D-5L), are needed to calculate quality-adjusted life-years (QALYs) for cost-effectiveness analyses. However, these are rarely used in clinical trials of interventions in axial spondyloarthritis (axSpA). In these cases, mapping can be used to predict HSUVs.

OBJECTIVE

To develop mapping algorithms to estimate EQ-5D-5L HSUVs from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).

METHODS

Data from the British Society for Rheumatology Biologics Register in Ankylosing Spondylitis (BSRBR-AS) provided 5122 observations with complete BASDAI, BASFI, and EQ-5D-5L responses covering the full range of disease severity. We compared direct mapping using adjusted limited dependent variable mixture models (ALDVMMs) and optional inclusion of the gap between full health and the next feasible value with indirect response mapping using ordered probit (OPROBIT) and generalised ordered probit (GOPROBIT) models. Explanatory variables included BASDAI, BASFI, and age. Metrics to assess model goodness-of-fit and performance/accuracy included Akaike and Bayesian information criteria (AIC/BIC), mean absolute error (MAE) and root mean square error (RMSE), plotting predictive vs. observed estimates across the range of BASDAI/BASFI and comparing simulated data with the original data set for the preferred/best model.

RESULTS

Overall, the ALDVMM models that did not formally include the gap between full health and the next feasible value outperformed those that did. The four-component mixture models (with squared terms included) performed better than the three-component models. Response mapping using GOPROBIT (no squared terms included) or OPROBIT (with squared terms included) offered the next best performing models after the three-component ALDVMM (with squared terms). Simulated data of the preferred model (ALDVMM with four-components) did not significantly underestimate uncertainty across most of the range of EQ-5D-5L values, however the proportion of data at full health was underrepresented, likely due in part to model fitting on a small number of observations at this point in the actual data (4%).

CONCLUSIONS

The mapping algorithms developed in this study enabled the generation of EQ-5D-5L utilities from BASDAI/BASFI. The indirect mapping equations reported for the EQ-5D-5L facilitate the calculation of the EQ-5D-5L utility scores using other UK and country-specific value sets.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Methodological Comparison of Mapping the Expanded Prostate Cancer Index Composite to EuroQoL-5D-3L Using Cross-Sectional and Longitudinal Data: Secondary Analysis of NRG/RTOG 0415

PURPOSE

To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data.

METHODS

This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities.

RESULTS

A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased.

CONCLUSION

Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.

A Review of Utility Measurement Methods Used in Pharmacoeconomic Submissions to HIRA in South Korea: Methodological Consistency and Areas for Improvement

Pharmacoeconomic (PE) guidelines, first published in 2006 and later updated in 2011, were developed to guide the preparation and submission of PE data to the Health Insurance Review and Assessment Service (HIRA) for drug reimbursement decision making in South Korea. This study, which was conducted as part of a project for revision of the PE guideline, reviewed utility values used in the PE submissions processed at HIRA during 2014-2018 to identify aspects of the current guidelines that may need to be revisited. A total of 50 PE submissions were processed at HIRA over the 5 years. Of these, 47 submissions that used quality-adjusted life-years as an outcome measure were included in this review. Data were extracted from full copies of the manufacturer's initial submissions and committee documents provided by HIRA. Of the 47 submissions, nearly half (n = 23, 48.9%) used published sources to obtain health state utility values, followed by direct methods using time trade-off (n = 7) or standard gamble (n = 2) and indirect methods with patient-level data using the EQ-5D-3L (n = 4) or the EQ-5D-5L (n = 2). Mapping, using the EQ-5D-3L as a target measure, was also adopted in six submissions, although it was somewhat unfavourably described in the guideline. Notably, 52.2% of the submissions with published sources took utility values from different sources for different health states defined in a single model. In addition, details of utility measurement methods or mapping functions taken from published sources were relatively poorly reported. Moreover, the preferences of the Korean general public, preferred by the guideline, were rarely reflected in the utility values used in submissions relying on published sources (95.7% for foreign values only/mixed) and mapping (66.7%). While most submissions with direct and indirect methods used domestic preference values, the former was occasionally criticised by assessment committees because of health state descriptions. This review highlights a considerable amount of inconsistency in the measurement of utility values used in the PE submissions during 2014-2018, indicating a strong need for methodological standardisation.

A video-feedback parenting intervention to prevent enduring behaviour problems in at-risk children aged 12-36 months: the Healthy Start, Happy Start RCT

BACKGROUND

Behaviour problems emerge early in childhood and place children at risk for later psychopathology.

OBJECTIVES

To evaluate the clinical effectiveness and cost-effectiveness of a parenting intervention to prevent enduring behaviour problems in young children.

DESIGN

A pragmatic, assessor-blinded, multisite, two-arm, parallel-group randomised controlled trial.

SETTING

Health visiting services in six NHS trusts in England.

PARTICIPANTS

A total of 300 at-risk children aged 12-36 months and their parents/caregivers.

INTERVENTIONS

Families were allocated in a 1 : 1 ratio to six sessions of Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD) plus usual care or usual care alone.

MAIN OUTCOME MEASURES

The primary outcome was the Preschool Parental Account of Children's Symptoms, which is a structured interview of behaviour symptoms. Secondary outcomes included caregiver-reported total problems on the Child Behaviour Checklist and the Strengths and Difficulties Questionnaire. The intervention effect was estimated using linear regression. Health and social care service use was recorded using the Child and Adolescent Service Use Schedule and cost-effectiveness was explored using the Preschool Parental Account of Children's Symptoms.

RESULTS

In total, 300 families were randomised: 151 to VIPP-SD plus usual care and 149 to usual care alone. Follow-up data were available for 286 (VIPP-SD, n = 140; usual care, n = 146) participants and 282 (VIPP-SD, n = 140; usual care, n = 142) participants at 5 and 24 months, respectively. At the post-treatment (primary outcome) follow-up, a group difference of 2.03 on Preschool Parental Account of Children's Symptoms (95% confidence interval 0.06 to 4.01; p = 0.04) indicated a positive treatment effect on behaviour problems (Cohen's d = 0.20, 95% confidence interval 0.01 to 0.40). The effect was strongest for children's conduct [1.61, 95% confidence interval 0.44 to 2.78; p = 0.007 (d = 0.30, 95% confidence interval 0.08 to 0.51)] versus attention deficit hyperactivity disorder symptoms [0.29, 95% confidence interval -1.06 to 1.65; p = 0.67 (d = 0.05, 95% confidence interval -0.17 to 0.27)]. The Child Behaviour Checklist [3.24, 95% confidence interval -0.06 to 6.54; p = 0.05 (d = 0.15, 95% confidence interval 0.00 to 0.31)] and the Strengths and Difficulties Questionnaire [0.93, 95% confidence interval -0.03 to 1.9; p = 0.06 (d = 0.18, 95% confidence interval -0.01 to 0.36)] demonstrated similar positive treatment effects to those found for the Preschool Parental Account of Children's Symptoms. At 24 months, the group difference on the Preschool Parental Account of Children's Symptoms was 1.73 [95% confidence interval -0.24 to 3.71; p = 0.08 (d = 0.17, 95% confidence interval -0.02 to 0.37)]; the effect remained strongest for conduct [1.07, 95% confidence interval -0.06 to 2.20; p = 0.06 (d = 0.20, 95% confidence interval -0.01 to 0.42)] versus attention deficit hyperactivity disorder symptoms [0.62, 95% confidence interval -0.60 to 1.84; p = 0.32 (d = 0.10, 95% confidence interval -0.10 to 0.30)], with little evidence of an effect on the Child Behaviour Checklist and the Strengths and Difficulties Questionnaire. The primary economic analysis showed better outcomes in the VIPP-SD group at 24 months, but also higher costs than the usual-care group (adjusted mean difference £1450, 95% confidence interval £619 to £2281). No treatment- or trial-related adverse events were reported. The probability of VIPP-SD being cost-effective compared with usual care at the 24-month follow-up increased as willingness to pay for improvements on the Preschool Parental Account of Children's Symptoms increased, with VIPP-SD having the higher probability of being cost-effective at willingness-to-pay values above £800 per 1-point improvement on the Preschool Parental Account of Children's Symptoms.

LIMITATIONS

The proportion of participants with graduate-level qualifications was higher than among the general public.

CONCLUSIONS

VIPP-SD is effective in reducing behaviour problems in young children when delivered by health visiting teams. Most of the effect of VIPP-SD appears to be retained over 24 months. However, we can be less certain about its value for money.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN58327365.

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. 25, No. 29. See the NIHR Journals Library website for further project information.

Estimation of losses of quality-adjusted life expectancy attributed to the combination of cognitive impairment and multimorbidity among Chinese adults aged 45 years and older

OBJECTIVES

This study aims to estimate the losses of quality-adjusted life expectancy (QALE) due to the joint effects of cognitive impairment and multimorbidity, and to further confirm additional losses attributable to this interaction among middle-aged and elderly Chinese people.

METHODS

The National Cause of Death Monitoring Data were linked with the China Health and Retirement Longitudinal Study (CHARLS). A mapping and assignment method was used to estimate health utility values, which were further used to calculate QALE. Losses of QALE were measured by comparing the differences between subgroups. All the losses of QALE were displayed at two levels: the individual and population levels.

RESULTS

At age 45, the individual-level and population-level losses of QALE attributed to the combination of cognitive impairment and multimorbidity were 7.61 (95% CI: 5.68, 9.57) years and 4.30 (95% CI: 3.43, 5.20) years, respectively. The losses for cognitive impairment alone were 3.10 (95% CI: 2.29, 3.95) years and 1.71 (95% CI: 1.32, 2.13) years at the two levels. Similarly, the losses for multimorbidity alone were 3.53 (95% CI: 2.53, 4.56) years and 1.91 (95% CI: 1.24, 2.63) years at the two levels. Additional losses due to the interaction of cognitive impairment and multimorbidity were indicated by the 0.98 years of the individual-level gap and 0.67 years of the population-level gap.

CONCLUSION

Among middle-aged and elderly Chinese people, cognitive impairment and multimorbidity resulted in substantial losses of QALE, and additional QALE losses were seen due to their interaction at both individual and population levels.

Health State Utility Values in Juvenile Idiopathic Arthritis: What is the Evidence?

OBJECTIVES

The objectives of this systematic review were to identify health state utility values (HSUV) of children and adults with juvenile idiopathic arthritis in the literature and to assess whether HSUV were appropriately reported and could be used to inform parameter inputs for a model-based cost-utility analysis to inform decision making.

METHODS

MEDLINE, EMBASE, PsycINFO, EconLit and CINAHL databases were searched in July 2019. Inclusion criteria were studies using preference-based instruments, targeting children or adults with juvenile idiopathic arthritis, and in the English language. The quality of studies was assessed using a modified checklist that included relevant sources of bias and assessment of quality of HSUV valuation and measurement. A descriptive analysis was conducted, including assessment on reporting of population characteristics and stratification of HSUV by potential health states or population subgroup.

RESULTS

From 620 identified articles, ten reported HSUV. Seven studies reported HSUV of children with juvenile idiopathic arthritis, and three of adults with a history of juvenile idiopathic arthritis. Population disease activity status and drug treatment were reported in less than half of the studies. Six (out of ten) studies stratified HSUV results for at least one of the potential health state categories, but they represent very specific situations or interventions (e.g. patients receiving different types of physiotherapy or treated with etanercept over time).

CONCLUSIONS

We have identified critical gaps in the literature reporting HSUV in patients with juvenile idiopathic arthritis including a lack of HSUV measures for distinct health states, particularly in adults with a history of juvenile idiopathic arthritis. The current reported HSUV data in juvenile idiopathic arthritis are insufficient for a full cost-utility analysis with a short or lifetime horizon.

Valuing health-related quality of life in heart failure: a systematic review of methods to derive quality-adjusted life years (QALYs) in trial-based cost-utility analyses

The accurate measurement of health-related quality of life (HRQoL) and the value of improving it for patients are essential for deriving quality-adjusted life years (QALYs) to inform treatment choice and resource allocation. The objective of this review was to identify and describe the approaches used to measure and value change in HRQoL in trial-based economic evaluations of heart failure interventions which derive QALYs as an outcome. Three databases (PubMed, CINAHL, Cochrane) were systematically searched. Twenty studies reporting economic evaluations based on 18 individual trials were identified. Most studies (n = 17) utilised generic preference-based measures to describe HRQoL and derive QALYs, commonly the EQ-5D-3L. Of these, three studies (from the same trial) also used mapping from a condition-specific to a generic measure. The remaining three studies used patients’ direct valuation of their own health or physician-reported outcomes to derive QALYs. Only 7 of the 20 studies reported significant incremental QALY gains. Most interventions were reported as being likely to be cost-effective at specified willingness to pay thresholds. The substantial variation in the approach applied to derive QALYs in the measurement of and value attributed to HRQoL in heart failure requires further investigation.

A quality-of-life mapping function developed from a grass pollen sublingual immunotherapy trial to a tree pollen sublingual immunotherapy trial

Aims: Allergic rhinitis is caused by sensitivity to environmental allergens that can significantly impact quality-of-life. The objective of this analysis was to estimate health state utilities and quality-adjusted life days (QALDs) for a tree allergy immunotherapy trial, TT-04 (EudraCT No.2015-004821-15). Health-state utilities are a measure of patient preference for health states and are necessary to derive QALDs for cost-utility analysis. Preference-based utilities were not collected in the TT-04 trial, so a mapping algorithm was developed based on a similar grass allergy immunotherapy trial, GT-08 (EudraCT No. 2004-000083-27), to estimate utilities.Methods: A two-part model was developed to predict utilities for the GT-08 trial and applied to the TT-04 trial to estimate the difference in mean utility and QALDs between SQ tree sublingual immunotherapy (SLIT)-tablet and placebo.Results: Mean utility difference between SQ tree SLIT-tablet and placebo was 0.030 [95% CI = 0.015-0.046] during the birch pollen season (BPS), 0.019 [95% CI = 0.007-0.030] during the tree pollen season (TPS) and 0.018 [95% CI = 0.007-0.030] during the full trial. The treatment showed a QALD benefit of 1.26 [95% CI = 0.619-1.917] during the BPS, 1.90 [95% CI = 0.692-3.047] during the TPS, and 2.47 [95% CI = 0.930-4.101] during the full trial.Limitations: The generalizability of this algorithm is limited to allergy trials containing the same covariates as those present in the model. The analysis also assumes that grass and tree pollen allergy have the same relationship with EQ5D utilities, which is supported by the fact that both grass and tree pollen induce similar symptoms.Conclusions: Application of the mapping function enabled the calculation of QALDs associated with the treatment, with the caveat that data were extrapolated from grass seasonal allergy to tree seasonal allergy. The results showed a significant QALD benefit of the treatment over placebo in treatment of tree pollen-induced rhinoconjunctivitis.

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).

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.

The Use of Health State Utility Values in Decision Models

Methodological issues of how to use health state utility values (HSUVs) in decision models arise frequently, including the most appropriate evidence to use as the baseline (e.g. the baseline HSUVs associated with avoiding a particular health condition or event), how to capture changes due to adverse events and how to appropriately capture uncertainty in progressive conditions where the expected change in quality of life is likely to be monotonically decreasing over time. As preference-based measures provide different values when collected from the same patient, it is important to ensure that all HSUVs used within a single model are obtained from the same instrument where ever possible. When people enter the model without the condition of interest (e.g. primary prevention of cardiovascular disease, screening or vaccination programmes), appropriate age- and gender-adjusted HSUVs from people without the particular condition should be used as the baseline. General population norms may be used as a proxy if the exact condition-specific evidence is not available. Individual discrete health states should be used for serious adverse reactions to treatment and the corresponding HSUVs sourced as normal. Care should be taken to avoid double counting when capturing the effects for both less severe adverse reactions (e.g. itchy skin rash or dry cough) and more severe adverse events (e.g. fatigue in oncology). Transparency in reporting standards for both the justification of the evidence used and any 'adjustments' is important to increase readers' confidence that the evidence used is the most appropriate available.

The Identification, Review and Synthesis of Health State Utility Values from the Literature

Systematic literature reviews of health-related quality of life (HRQoL) evidence that are to inform economic models can be challenging due to the volume of hits identified in searches using generic terms for HRQoL. Nevertheless, a robust review of the literature is required to ensure that the health state utility values (HSUVs) used in the economic model are the most appropriate available. This article provides a synopsis of literature relating to identifying, reviewing and synthesising HSUVs. The process begins with scoping the needs of the economic model, including the definitions of health states and the requirements of any reimbursement agencies. A sequence of searches may be required as the economic model evolves. The terminology used for HRQoL measures may be problematic, and as there is no robust HRQoL filter [equivalent to that applied for randomised control trial (RCTs)], sifting the results of sensitive searches can be resource intensive. Alternative approaches such as forward and backward citation searches may reduce the resources required, while maintaining the integrity of the search. Any included studies should be assessed in terms of quality using a recommended checklist, and insufficient detail in the primary studies should be noted as a short-coming in this exercise. Subject to homogeneity (similar populations, same measure and preference weights) evidence can be pooled in some way, although methodological research into the appropriateness of alternative techniques for meta-analysis is in its infancy. Reporting standards are key and as a minimum should include details on searches, inclusion/exclusion criteria (together with rationale for exclusion at each stage), assessment of quality and relevance of included studies, and justification for the choice of final HSUVs.

International Regulations and Recommendations for Utility Data for Health Technology Assessment

Recommendations and guidelines for the collection, generation, source and usage of utility data for health technology assessment (HTA) vary across different countries, with no international consensus. Many international agencies generate their own guidelines providing details on their preferred methods for HTA submissions, and there is variability in both what they recommend and the clarity and amount of detail provided in their guidelines. This article provides an overview of international regulations and recommendations for utility data in HTA for a selection of key HTA countries: Australia, Canada, France, Germany, the Netherlands, Spain (Catalonia), Sweden and the UK (England/Wales and Scotland). International guidelines are typically clear and detailed for the selection of countries assessed regarding the source description of health states (e.g. generic preference-based measure) and who should provide preference weights for these health states (e.g. general population for own country). Many guidelines specify the use of off-the-shelf generic preference-based measures, and some further specify a measure, such as EQ-5D. However, international guidelines are either unclear or lack detailed guidance regarding the collection (e.g. patients report own health), source (e.g. clinical trial) and usage (e.g. adjusting for comorbidities) of utility values. It is argued that there is a need for transparent and detailed international guidelines on utility data recommendations to provide decision makers with the best possible evidence. Where this is not possible it is recommended that best practice should be used to inform the collection, source and usage of utility values in HTA.

Recommended Methods for the Collection of Health State Utility Value Evidence in Clinical Studies

A conceptual model framework and an initial literature review are invaluable when considering what health state utility values (HSUVs) are required to populate health states in decision models. They are the recommended starting point early within a research and development programme, and before development of phase III trial protocols. While clinical trials can provide an opportunity to collect the required evidence, their appropriateness should be reviewed against the requirements of the model structure taking into account population characteristics, time horizon and frequency of clinical events. Alternative sources such as observational studies or registries may be more appropriate when evidence describing changes in HSUVs over time or rare clinical events is required. Phase IV clinical studies may provide the opportunity to collect additional longitudinal real-world evidence. Aspects to consider when designing the collection of the evidence include patient and investigator burden, whom to ask, the representativeness of the population, the exact definitions of health states within the economic model, the timing of data collection, sample size, and mode of administration. Missing data can be an issue, particularly in longitudinal studies, and it is important to determine whether the missing data will bias inferences from analyses. For example, respondents may fail to complete follow-up questionnaires because of a relapse or the severity of their condition. The decision on the preferred study type and the particular quality of life measure should be informed by any evidence currently available in the literature, the design of data collection, and the exact requirements of the model that will be used to support resource allocation decisions (e.g. reimbursement).