Predicting EQ-5D-US and SF-6D societal health state values from the Osteoporosis Assessment Questionnaire

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.

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.

Prevalence of dyspareunia and vulvodynia in a Colombian clinic sample: A pilot survey

Objective:

The primary objective of our research was to determine the prevalence of dyspareunia and vulvodynia in a limited clinical sample of Colombian women.

Methods:

The study was composed of women who were seen in local Colombian medical clinics. A pilot survey was designed in Spanish to obtain patients’ demographic and medical history. Harlow’s validated questionnaire was incorporated into our survey to screen for dyspareunia and vulvodynia.

Results:

Five hundred and eighty-two women completed the survey and 96.2% (N = 560) reported being sexually active. The survey response rate was 96%. Among those who were initially screened and reported being sexually active, 51.8% (N = 290; 95% confidence interval: 47.7%–55.9%) described having pain with intercourse (dyspareunia) and 25.9% (N = 145; 95% confidence interval: 22.4%–29.7%) did not have pain. Of those who were sexually active, Harlow’s validated questionnaire was completed by 490 women: 27.6% (N = 135; 95% confidence interval: 23.7–31.75) described vulvar pain lasting longer than 3 months (vulvodynia); 51.9% (95% confidence interval: 43.1%–60.5%) had burning pain; 42.4% (95% confidence interval: 33.8%–51.0%) had sharp stabbing pain; 69.6% (95% confidence interval: 61.1%–77.2%) reported having pain during intercourse (dyspareunia).

Conclusion:

In this select population sample, the prevalence of dyspareunia ranged from 51.8% using a general survey to 69.6% using a validated questionnaire. The prevalence of vulvodynia was 27.6% (95% confidence interval: 23.7–31.75). This is higher than the prevalence of dyspareunia (45%) and vulvodynia (3%–16%) reported in the US general population.

Patient-centered assessment on disease burden, quality of life, and treatment satisfaction associated with acromegaly

The study aimed to assess the economic burden, health-related quality of life (HRQoL), and acromegaly treatment satisfaction in the USA. A web-based, cross-sectional survey was distributed to members of Acromegaly Community. Data related to comorbidities, treatment patterns, and treatment satisfaction were collected. The costs over the past 3 months included out-of-pocket cost, sick leave, leave of absence, direct loss of job due to acromegaly, unemployment, assistance to perform household chores, and family member loss of income. The HRQoL was assessed by Acromegaly Quality of Life (AcroQoL) and EQ-5D-3L questionnaires. Among 106 patients who completed the survey (mean age: 46 years, female: 76.4%), 44.3% presented with ≥5 comorbidities, and 90.6% reporting acromegaly-related symptoms. Compared with the low-symptom group 0-3 (n=41), the 4+ symptoms group (n=65) was more likely to have depression (OR=2.3, 95% CI 1.1 to 5.2) and cardiovascular disease (OR=5.8, 95% CI 2.0 to 16.7), and experienced higher costs (loss of job: $8874 vs $1717, P=0.02; unemployment disability: $17,102 vs $429, P=0.003; household chores: $2160 vs $932, P=0.0003; family members' income loss: $692 vs $122, P=0.03). The high-symptom group had lower HRQoL scores, compared with the low-symptom group (EQ-5D-3L: 0.53 vs 0.75, P<0.0001; AcroQoL: 27 vs 56, P<0.0001). Only 55.7% among patients requiring injections for acromegaly were satisfied. Patients with acromegaly who presented with multiple acromegaly-related symptoms were evidenced to have experienced higher economic burden and poorer quality of life than patients with the same diagnosis but fewer symptoms. The low rate of treatment satisfaction warrants need for further studies.

Should additional domains be added to the EQ-5D health-related quality of life instrument for community-based studies? An analytical descriptive study

Background

There is increasing interest in monitoring the health-related quality of life (HRQoL) of populations as opposed to clinical populations. The EQ-5D identifies five domains as being most able to capture the HRQoL construct. The question arises as to whether these domains are adequate within a community-based population or whether additional domains would add to the explanatory power of the instrument.

Methods

As part of a community-based survey, the responses of 310 informants who reported at least one problem in one domain filled in the EQ-5D three-level version and the WHOQOL-BREF (World Health Organization Quality of Life Scale – Abbreviated version). Using the EQ-5D visual analogue scale (VAS) of rating of health as a dependent variable, the five EQ-5D and four selected WHOQOL-BREF items were entered as dummy variables in multiple regression analysis.

Results

The additional domains increased the explanatory power of the model from 52 % (EQ-5D only) to 57 % (all domains). The coefficients of Self-Care and Usual Activities were not significant in any model. The most parsimonious model included the EQ-5D domains of Mobility, Pain/Discomfort, Anxiety/Depression, Concentration, and Sleep (adjusted r² = .57).

Conclusions

The EQ-5D-3L performed well, but the addition of domains such as Concentration and Sleep increased the explanatory power. The user needs to weigh the advantage of using the EQ-5D, which allows for the calculation of a single summary index, against the use of a set of domains that are likely to be more responsive to differences in HRQoL within community living respondents. The poor predictive power of the Self-Care and Usual Activities domains within this context needs to be further examined.

Use of health-related quality of life measures to predict health utility in postmenopausal osteoporotic women: results from the Multiple Outcomes of Raloxifene Evaluation study

BACKGROUND

The aim of this study is to examine the associations between health utility (HU), health-related quality of life (HRQoL), and patient characteristics in postmenopausal osteoporotic (PMO) women.

METHODS

Baseline data from a subsample of 1,245 participants of the Multiple Outcomes of Raloxifene Evaluation study, a randomized, placebo-controlled, multinational clinical trial to evaluate the safety and efficacy of raloxifene, were analyzed. The study cohort included 694 participants from non-European Union (non-EU) countries and 551 participants from EU countries. All participants with complete baseline HU and HRQoL assessments were included in the following analyses: 1) HU (HUI or EQ-5D) and HRQoL (QualEFFO or OPAQ and NHP) associations; 2) HU variability explained by HRQoL domains; and 3) the percentage of HU variability explained by statistically significant (p < 0.05) HRQoL domains, after adjusting for baseline characteristics.

RESULTS

Several domains were significantly associated with HU scores. HU variance was well explained (41% to 61%) by 4 to 6 (p < 0.05) significant HRQoL domains. After controlling for baseline characteristics, 48% to 64% of the HU variance was well explained by 5 to 7 significant (p < 0.05) HRQoL domains. Additional trend analyses detected statistically significant decreases in HRQoL and HU scores with an increased number of vertebral and non-vertebral fractures.

CONCLUSIONS

Both disease-targeted and generic HRQoL domains were well correlated with HU. A large percentage (48% to 64%) of the HU variance was explained by HRQoL, after adjusting for baseline characteristics. Both disease-targeted and generic HRQoL measures were significant predictors of HU. HRQoL and HU scores decreased with increased vertebral and non-vertebral fractures.