Mapping between the Roland Morris Questionnaire and Generic Preference-Based Measures

ObjectivesThe Roland Morris Questionnaire (RMQ) is a widely used health status measure for low back pain (LBP). It is not preference-based, and there are currently no established algorithms for mapping between the RMQ and preference-based health-related quality-of-life measures. Using data from randomized controlled trials of treatment for LBP, we sought to develop algorithms for mapping between RMQ scores and health utilities derived using either the EuroQol five-dimensional questionnaire (EQ-5D) or the six-dimensional health state short form (derived from Medical Outcomes Study 36-Item Short-Form Health Survey) (SF-6D).MethodsThis study is based on data from the Back Skills Training Trial in which data were collected from 701 patients at baseline and subsequently at 3, 6, and 12 months postrandomization using a range of outcome measures, including the RMQ, the EQ-5D, and the Short Form 12 item Health Survey (SF-12) (from which SF-6D utilities can be derived). We used baseline trial data to estimate models using both direct and response mapping approaches to predict EQ-5D and SF-6D health utilities and dimension responses. A multistage model selection process was used to assess the predictive accuracy of the models. We then explored different techniques and mapping models that made use of repeated follow-up observations in the data. The estimated mapping algorithms were validated using external data from the UK Back Pain Exercise and Manipulation trial.ResultsA number of models were developed that accurately predict health utilities in this context. The best performing model for RMQ to EQ-5D mapping was a beta regression with Bayesian quasi-likelihood estimation that included 24 dummy variables for RMQ responses, age, and sex as covariates (mean squared error 0.0380) based on repeated data. The model selected for RMQ to SF-6D mapping was a finite mixture model that included the overall RMQ score, age, sex, RMQ score squared, age squared, and an interaction term for age and RMQ score as covariates (mean squared error 0.0114) based on repeated data.ConclusionsIt is possible to reasonably predict EQ-5D and SF-6D health utilities from RMQ scores and responses using regression methods. Our regression equations provide an empirical basis for estimating health utilities when EQ-5D or SF-6D data are not available. They can be used to inform future economic evaluations of interventions targeting LBP.     

Mapping the Cancer-Specific EORTC QLQ-C30 to the Preference-Based EQ-5D, SF-6D, and 15D Instruments

Objectives:  To estimate models, via ordinary least squares regression, for predicting Euro Qol 5D (EQ-5D), Short Form 6D (SF-6D), and 15D utilities from scale scores of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30).
Methods:  Forty-eight gastric cancer patients, split up into equal subgroups by age, sex, and chemotherapy scheme, were interviewed, and the survey included the QLQ-C30, SF-36, EQ-5D, and 15D instruments, along with sociodemographic and clinical data. Model predictive ability and explanatory power were assessed by root mean square error (RMSE) and adjusted R ² values, respectively. Pearson's r between predicted and reported utility indices was compared. Three random subsamples, half in size the initial sample, were created and used for "external" validation of the modeling equations.
Results:  Explanatory power was high, with adjusted R ² reaching 0.909, 0.833, and 0.611 for 15D, SF-6D, and EQ-5D, respectively. After normalization of RMSE to the range of possible values, the prediction errors were 12.0, 5.4, and 5.6% for EQ-5D, SF-6D, and 15D, respectively. The estimation equations produced a range of utility scores similar to those achievable by the standard scoring algorithms. Predicted and reported indices from the validation samples were comparable thus confirming the previous results.
Conclusions:  Evidence on the ability of QLQ-C30 scale scores to validly predict 15D and SF-6D utilities, and to a lesser extent, EQ-5D, has been provided. The modeling equations must be tried in future studies with larger and more diverse samples to confirm their appropriateness for estimating quality-adjusted life-year in cancer-patient trials including only the QLQ-C30.     

An investigation into the empirical validity of the EQ-5D and SF-6D based on hypothetical preferences in a general population

BACKGROUND: An important consideration for studies that derive utility scores using multi-attribute utility measures is the psychometric integrity of the measurement instrument. Of particular importance is the requirement to establish the empirical validity of multi-attribute utility measures; that is, whether they generate utility scores that, in practice, reflect people's preferences. We compared the empirical validity of EQ-5D versus SF-6D utility scores based on hypothetical preferences in a large, representative sample of the English population. METHODS: Adult participants in the 1996 Health Survey for England (n=16 443) formed the basis of the investigation. The subjects were asked to complete the EQ-5D and SF-36 measures. Their responses were converted into utility scores using the York A1 tariff set and the SF-6D utility algorithm, respectively. One-way analysis of variance was used to test the hypothetically constructed preference rule that each set of utility scores differs significantly by self-reported health status (categorised as very good, good, fair, bad or very bad). The degree to which EQ-5D and SF-6D utility scores reflect alternative configurations of self-reported health status; illness, disability or infirmity, and medication use was tested using the relative efficiency statistic and receiver operating characteristic (ROC) curves. RESULTS: The mean utility score for the EQ-5D was 0.845 (95% CI: 0.842, 0.849), whilst the mean utility score for the SF-6D was 0.799 (95% CI: 0.797, 0.802), representing a mean difference in utility score of 0.046 (95% CI: 0.044, 0.049; p<0.001). Bland-Altman plots displayed considerable lack of agreement between the two measures, particularly at the lower end of the utility scale. Both measures demonstrated statistically significant differences between subjects who described their health status as very good, good, fair, bad or very bad (p<0.001), as well as monotonically decreasing utility scores (test for linear trend: p<0.001). The SF-6D was between 30.9 and 100.4% more efficient than the EQ-5D at detecting differences in self-reported health status, and between 10.4 and 45.6% more efficient at detecting differences in illness, disability or infirmity and medication use. The area under the curve scores generated by the ROC curves were significantly higher for the SF-6D at the 0.1% significance level when self-reported health status was dichotomised as very good versus good, fair, bad or very bad. However, the AUC scores did not reveal any significant differences in the discriminatory powers of the measures when alternative configurations of illness, disability or infirmity and medication use were examined. CONCLUSIONS: This study provides evidence that the SF-6D is an empirically valid and efficient alternative multi-attribute utility measure to the EQ-5D, and is capable of discriminating between external indicators of health status. However, health economists should also consider other psychometric properties, such as practicality and reliability, when selecting either measure for evaluative purposes.     

Comparison of EQ-5D and SF-6D utilities in mental health patients

Various preference-based measures of health are available for use as an outcome measure in cost-utility analysis. The aim of this study is to compare two such measures EQ-5D and SF-6D in mental health patients. Baseline data from a Dutch multi-centre randomised trial of 616 patients with mood and/or anxiety disorders were used. Mean and median EQ-5D and SF-6D utilities were compared, both in the total sample and between severity subgroups based on quartiles of SCL-90 scores. Utilities were expected to decline with increased severity.Both EQ-5D and SF-6D utilities differed significantly between patients of adjacent severity groups. Mean utilities increased from 0.51 at baseline to 0.68 at 1.5 years follow-up for EQ-5D and from 0.58 to 0.70 for SF-6D. For all severity subgroups, the mean change in EQ-5D utilities as well as in SF-6D utilities was statistically significant. Standardised response means were higher for SF-6D utilities. We concluded that both EQ-5D and SF-6D discriminated between severity subgroups and captured improvements in health over time. However, the use of EQ-5D resulted in larger health gains and consequent lower cost-utility ratios, especially for the subgroup with the highest severity of mental health problems.     

Mapping Utility Scores from a Disease-Specific Quality-of-Life Measure in Bariatric Surgery Patients

Objectives:  To develop algorithms for a conversion of disease-specific quality-of-life into health state values for morbidly obese patients before or after bariatric surgery.
Methods:  A total of 893 patients were enrolled in a prospective cross-sectional multicenter study. In addition to demographic and clinical data, health-related quality-of-life (HRQoL) data were collected using the disease-specific Moorehead-Ardelt II questionnaire (MA-II) and two generic questionnaires, the EuroQoL-5D (EQ-5D) and the Short Form-6D (SF-6D). Multiple regression models were constructed to predict EQ-5D- and SF-6D-based utility values from MA-II scores and additional demographic variables.
Results:  The mean body mass index was 39.4, and 591 patients (66%) had already undergone surgery. The average EQ-5D and SF-6D scores were 0.830 and 0.699. The MA-IIwas correlated to both utility measures (Spearman's r  = 0.677 and 0.741). Goodness-of-fit was highest ( R ² = 0.55 in the validation sample) for the following item-based transformation algorithm: utility (MA-II-based) = 0.4293 + (0.0336 × MA1) + (0.0071 × MA2) + (0.0053 × MA3) + (0.0107 × MA4) + (0.0001 × MA5). This EQ-5D-based mapping algorithm outperformed a similar SF-6D-based algorithm in terms of mean absolute percentage error ( P  = 0.045).
Conclusions:  Because the mapping algorithm estimated utilities with only minor errors, it appears to be a valid method for calculating health state values in cost-utility analyses. The algorithm will help to define the role of bariatric surgery in morbid obesity.     

Predicting EuroQoL EQ-5D preference scores from the SF-12 Health Survey in a nationally representative sample.

PURPOSE: To predict the EuroQoL EQ-5D utility index from the SF-12 Health Survey for a US national sample of adults. METHODS: The authors used the 2000 Medical Expenditure Panel Survey to examine the relationship between instruments. Linear regression was used to predict EQ-5D scores from Physical Component Summary (PCS) and Mental Component Summary (MCS) scores of the SF-12. A prediction model was derived in one half of the sample and validated in the other half. RESULTS: Complete responses to both measures were available for 14,580 adults; 7313 (50.2%) surveys were used for the derivation set. The 2-variable model predicted 61% of the variance in EQ-5D scores and provided reasonable ability to predict mean EQ-5D scores from mean PCS and MCS scores. Confidence intervals are dependent on sample size and variance of PCS and MCS scores. CONCLUSIONS: EQ-5D scores can be reasonably predicted from the SF-12. This model allows researchers to estimate utility data for use in decision and cost-utility analyses.     

Estimating health utilities in patients with asthma and COPD: evidence on the performance of EQ-5D and SF-6D

Objective  The objective of this study was to understand systematic differences in utility values derived from the EQ-5D and the SF-6D in two respiratory populations with heterogeneous disease severity. Methods  This study involved secondary analysis of data from two cross-sectional surveys of patients with asthma (N = 228; Hungary) and COPD (N = 176; Sweden). Disease severity was defined according to GINA and GOLD guidelines for asthma and COPD, respectively. EQ-5D and SF-6D scores and their distributional characteristics were compared across the two samples by disease severity level. Results  Within each patient population, mean EQ-5D and SF-6D scores were similar for the overall group and for those with moderate disease. Mean scores varied for patients with mild and severe disease. EQ-5D versus SF-6D scores in the asthma group by severity levels were 0.89 versus 0.80, 0.70 versus 0.73, 0.63 versus 0.64, and 0.51 versus 0.63, respectively. EQ-5D versus SF-6D scores in the COPD group by severity levels were 0.85 versus 0.80, 0.73 versus 0.73, 0.74 versus 0.73, and 0.53 versus 0.62, respectively. Conclusions  Results suggest the EQ-5D and SF-6D do not yield consistent utility values in patients with asthma and COPD due to differences in underlying valuation techniques and the EQ-5D’s limited response options relative to mild disease.     

A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index

BACKGROUND: The SF-6D is a new health state classification and utility scoring system based on 6 dimensions ('6D') of the Short Form 36, and permits a "bridging" transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3. METHODS: Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC). RESULTS: SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60-0.63) and 0.58 (95% CI 0.54-0.62) respectively; a difference of 0.03 (p<0.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30-0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48-0.68) and agreement by ICC was 0.42 (95% CI 0.31-0.52). Correlations between dimensions of SF-6D were higher than for HUI3. CONCLUSIONS: Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities.     

An empirical comparison of EQ-5D and SF-6D in liver transplant patients

There remains disagreement about the preferred utility-based measure of health-related quality of life for use in constructing quality-adjusted life years (QALYs). The recent development of a new measure, the SF-6D, has highlighted this issue. The SF-6D and EuroQol EQ-5D measure health-related utilities on a scale where 0 represents death and 1 represents full health, and both have utility scores generated from random samples of the general UK population. This study explored whether, in a large sample of liver transplant patients, the two instruments provide similar results. The empirical data highlight important variation in the results generated from the use of the two instruments. The data are consistent with a view that the SF-6D does not describe health states at the lower end of the utility scale but is more sensitive than EQ-5D in detecting small changes towards the top of the scale.     

A comparison of the performance of the EQ-5D and SF-6D for individuals aged >or= 45 years

We sought to compare the performance of the EQ-5D and SF-6D with regard to the criteria of practicality, convergent validity, and construct validity, the level of agreement between the two measures was also assessed. Responses from 1865 individuals aged >or= 45 years in one general practice were analysed. Of these, 93.1% completed the EQ-5D, compared with 86.4% for the SF-6D, where individuals who were older, female, of a lower occupational skill level, from an area of lower deprivation, or used prescribed medication were significantly less likely to complete the SF-6D. The performance of both measures was comparable with regard to both convergent and construct validities, as both the EQ-5D and SF-6D scores were closely related to scores on the EuroQol visual analogue scale (VAS) (p<0.001) and able to discriminate between people who did and did not take: (i) analgesics and (ii) other prescribed medication. Despite EQ-5D and SF-6D scores being highly correlated (p<0.001), individuals who were healthier (according to the VAS) had higher mean scores on the EQ-5D (p<0.001), whereas less healthy individuals had higher mean scores on the SF-6D (individuals with knee pain, osteoarthritis, back pain, rheumatoid arthritis, and hip pain had significantly lower mean scores on the EQ-5D, p<0.001).     

Utility scores for different health states related to depression: individual participant data analysis

Objectives

Depression is associated with considerable impairments in health-related quality-of-life. However, the relationship between different health states related to depression severity and utility scores is unclear. The aim of this study was to evaluate whether utility scores are different for various health states related to depression severity.

Methods

We gathered individual participant data from ten randomized controlled trials evaluating depression treatments. The UK EQ-5D and SF-6D tariffs were used to generate utility scores. We defined five health states that were proposed from American Psychiatric Association and National Institute for Clinical Excellence guidelines: remission, minor depression, mild depression, moderate depression, and severe depression. We performed multilevel linear regression analysis.

Results

We included 1629 participants in the analyses. The average EQ-5D utility scores for the five health states were 0.70 (95% CI 0.67–0.73) for remission, 0.62 (95% CI 0.58–0.65) for minor depression, 0.57 (95% CI 0.54–0.61) for mild depression, 0.52 (95%CI 0.49–0.56) for moderate depression, and 0.39 (95% CI 0.35–0.43) for severe depression. In comparison with the EQ-5D, the utility scores based on the SF-6D were similar for remission (EQ-5D?=?0.70 vs. SF-6D?=?0.69), but higher for severe depression (EQ-5D?=?0.39 vs. SF-6D?=?0.55).

Conclusions

We observed statistically significant differences in utility scores between depression health states. Individuals with less severe depressive symptoms had on average statistically significant higher utility scores than individuals suffering from more severe depressive symptomatology. In the present study, EQ-5D had a larger range of values as compared to SF-6D.

     

EQ-5D和SF-6D测量同期健康效用值的比较研究

目的:比较EQ-5D和SF-6D两个量表的适用性,为药物经济学评价过程中效用值测量工具的选择提供参考。方法:基于已有文献,从效用均值的分布、差异、可交换性及适用性、敏感性分析四个方面,对两个量表进行比较。结果:EQ-5D的效用均值略呈左偏分布,SF-6D较符合正态分布;两个量表在不同分段的效用均值存在差异,一致性较差。结论:两个量表不具有可互换性,在应用过程中应充分考虑两者差异,根据疾病特点选择合适的量表,SF-6D多适用于进程缓慢的疾病,EQ-5D多适用于测量较差的健康状态。     

Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D

Background: The SF-6D and EQ-5D are both preference-based measures of health. Empirical work is required to determine what the smallest change is in utility scores that can be regarded as important and whether this change in utility value is constant across measures and conditions. Objectives: To use distribution and anchor-based methods to determine and compare the minimally important difference (MID) for the SF-6D and EQ-5D for various datasets. Methods: The SF-6D is scored on a 0.29–1.00 scale and the EQ-5D on a −0.59–1.00 scale, with a score of 1.00 on both, indicating ‘full health’. Patients were followed for a period of time, then asked, using question 2 of the SF-36 as our anchor, if their general health is much better (5), somewhat better (4), stayed the same (3), somewhat worse (2) or much worse (1) compared to the last time they were assessed. We considered patients whose global rating score was 4 or 2 as having experienced some change equivalent to the MID. This paper describes and compares the MID and standardised response mean (SRM) for the SF-6D and EQ-5D from eight longitudinal studies in 11 patient groups that used both instruments. Results: From the 11 reviewed studies, the MID for the SF-6D ranged from 0.011 to 0.097, mean 0.041. The corresponding SRMs ranged from 0.12 to 0.87, mean 0.39 and were mainly in the ‘small to moderate’ range using Cohen’s criteria, supporting the MID results. The mean MID for the EQ-5D was 0.074 (range −0.011–0.140) and the SRMs ranged from −0.05 to 0.43, mean 0.24. The mean MID for the EQ-5D was almost double that of the mean MID for the SF-6D. Conclusions: There is evidence that the MID for these two utility measures are not equal and differ in absolute values. The EQ-5D scale has approximately twice the range of the SF-6D scale. Therefore, the estimates of the MID for each scale appear to be proportionally equivalent in the context of the range of utility scores for each scale. Further empirical work is required to see whether or not this holds true for other utility measures, patient groups and populations.     

Using the Multiple Sclerosis Impact Scale to Estimate Health State Utility Values: Mapping from the MSIS-29, Version 2, to the EQ-5D and the SF-6D

ObjectivesThe 29-item Multiple Sclerosis Impact Scale (MSIS-29) is a psychometrically validated patient-reported outcome measure increasingly used in trials of treatments for multiple sclerosis. However, it is non–preference-based and not amenable for use across policy decision-making contexts. Our objective was to statistically map from the MSIS-29, version 2, to the EuroQol five-dimension (EQ-5D) and the six-dimension health state short form (derived from short form 36 health survey) (SF-6D) to estimate algorithms for use in cost-effectiveness analyses.MethodsThe relationships between MSIS-29, version 2, and EQ-5D and SF-6D scores were estimated by using data from a cohort of people with multiple sclerosis in South West England (n=672). Six ordinary least squares (OLS), Tobit, and censored least adjusted deviation (CLAD) regression analyses were conducted on estimation samples, including the use of subscale and item scores, squared and interaction terms, and demographics. Algorithms from models with the smallest estimation errors (mean absolute error [MAE], root mean square error [RMSE], normalized RMSE) were then assessed by using separate validation samples.ResultsTobit and CLAD. For the EQ-5D, the OLS models including subscale squared terms, and item scores and demographics performed comparably (MAE 0.147, RMSE 0.202 and MAE 0.147, RMSE 0.203, respectively), and estimated scores well up to 3 years post-baseline. Estimation errors for the SF-6D were smaller (OLS model including squared terms: MAE 0.058, RMSE 0.073; OLS model using item scores and demographics: MAE 0.059, RMSE 0.08), and the errors for poorer health states found with the EQ-5D were less pronounced.ConclusionsWe have provided algorithms for the estimation of health state utility values, both the EQ-5D and SF-6D, from scores on the MSIS-29, version 2. Further research is now needed to determine how these algorithms perform in practical decision-making contexts, when compared with observed EQ-5D and SF-6D values.     

EQ-5D Versus SF-12 in Coronary Patients: Are They Interchangeable?

ObjectivesThe aim of this study was to compare EuroQol five-dimensional (EQ-5D) utility scores and six-dimensional health state classification (SF-6D) utility scores (derived from the 12-Item Short-Form Health Survey [SF-12]) by using a large European sample of patients with stable coronary heart disease. Special attention was given to country-specific results.MethodsData from the EURopean Action on Secondary and Primary Prevention by Intervention to Reduce Events III (EUROASPIRE III) survey were used. Patients hospitalized for a coronary artery bypass graft, percutaneous coronary intervention, acute myocardial infarction, or myocardial ischemia were interviewed and examined at least 6 months after their acute event. Health-related quality of life was assessed by using the EQ-5D and the SF-12. SF-12 outcomes were converted to SF-6D utility values, allowing comparison between both measures.ResultsBoth EQ-5D and SF-6D results were available for 7472 patients with coronary heart disease from 20 European countries. The measures were significantly correlated (intraclass correlation coefficient = 0.536); however, large differences between the two measures remain. A total of 28.8% of the patients reported a ceiling effect on the EQ-5D instrument, whereas only 4.2% of the patients reported full health based on the SF-6D. Especially the mental component does not seem to be completely captured by the EQ-5D instrument. Furthermore, patients with worse EQ-5D outcomes were more likely to have better SF-6D results, whereas patients with better EQ-5D outcomes were more likely to have worse SF-6D results.ConclusionsBoth measures are not interchangeable. Whereas the main disadvantage of the EQ-5D questionnaire is its ceiling effect, the potential advantages of SF-12 might disappear when converting the outcomes into an SF-6D utility, because of the small differences between patients.     

Assessing Differences in Utility Scores: A Comparison of Four Widely Used Preference-Based Instruments

OBJECTIVES: To characterize the differences in utility scores (dUTY) among four commonly used preference-based Health-Related Quality of Life instruments, to evaluate the potential impact of these differences on cost-utility analyses (CUA), and to determine if sociodemographic/clinical factors influenced the magnitude of these differences. METHODS: Consenting adult Chinese, Malay and Indian subjects in Singapore were interviewed using Singapore English, Chinese, Malay or Tamil versions of the EQ-5D, Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), and SF-6D. Agreement between instruments was assessed using Bland-Altman (BA) plots. Changes in incremental cost-utility ratio (ICUR) from dUTY were investigated using eight hypothetical decision trees. The influence of sociodemographic/clinical factors on dUTY between instrument pairs was studied using multiple linear regression (MLR) models for English-speaking subjects (circumventing structural zero issues). RESULTS: In 667 subjects (median age 48 years, 59% female), median utility scores ranged from 0.80 (95% confidence interval [CI] 0.80, 0.85) for the EQ-5D to 0.89 (95% CI 0.88, 0.89) for the SF-6D. BA plots: Mean differences (95% CI) exceeded the clinically important difference (CID) of 0.04 for four of six pairwise comparisons, with the exception of the HUI2/EQ-5D (0.03, CI: 0.02, 0.04) and SF-6D/HUI2 (0.02, CI: 0.006, 0.02). Decision trees: The ICER ranged from $94,661/QALY (quality-adjusted life-year; 6.3% difference from base case) to 100,693 dollars/QALY (0.3% difference from base case). MLR: Chronic medical conditions, marital status, and Family Functioning Measures scores significantly (P-value < 0.05) influenced dUTY for several instrument pairs. CONCLUSION: Although CIDs in utility measurements were present for different preference-based instruments, the impact of these differences on CUA appeared relatively minor. Chronic medical conditions, marital status, and family functioning influenced the magnitude of these differences.     

Prediction of Health Utility Scores in Patients with Chronic Hepatitis C Using the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV)

Background

Preference-based health utilities are used in economic analyses of disease burden and health care interventions. When specifically designed instruments cannot be applied, mapping algorithms for non–preference-based instruments can be used for prediction of health utility scores.