Are Current Reporting Standards Used to Describe Health State Utilities in Cost-Effectiveness Models Satisfactory?

OBJECTIVE: The aims of this study were to examine current reporting standards of health state utilities (HSU) using a review of published cost-effectiveness analyses in cardiovascular disease and to explore the impact of variation in model inputs used in these on estimated quality-adjusted life-years (QALYs) and cost-effectiveness. METHODS: Key health/economics bibliographic databases were searched to identify relevant articles published after 2014. Any narrative or values relating to the HSU used in the model were extracted and reviewed. The HSUs were systematically applied to an existing model to explore the influence of different values on QALYs and the incremental cost-effectiveness ratio. RESULTS: Twenty-four peer-reviewed articles were identified. Only 2 studies referred to a literature review for the HSUs. Most (18 of 24) referenced previously published economic studies (as opposed to the original source) for at least 1 of the HSUs. Only 4 studies referenced the original sources and reported all of the HSUs accurately, and several did not provide all the HSUs. Little information was provided on the methods used to calculate QALYs, for example, the duration of time for acute HSUs, what the baseline HSU was, the method that was used to assign HSUs for subsequent different events, or how constant HSUs for clinical events were combined with age-adjusted baseline values. The huge differences in HSUs used in the studies produced substantial variations in the QALYs and incremental cost-effectiveness ratios generated from the cost-effectiveness model. CONCLUSION: Current standards are poor, and there is a need for greater transparency in reporting the HSUs used in cost-effectiveness models.

Identification, Review, and Use of Health State Utilities in Cost-Effectiveness Models: An ISPOR Good Practices for Outcomes Research Task Force Report.

Cost-effectiveness models that present results in terms of cost per quality-adjusted life-year for health technologies are used to inform policy decisions in many parts of the world. Health state utilities (HSUs) are required to calculate the quality-adjusted life-years. Even when clinical studies assessing the effectiveness of health technologies collect data on HSUs to populate a cost-effectiveness model, which rarely happens, analysts typically need to identify at least some additional HSUs from alternative sources. When possible, HSUs are identified by a systematic review of the literature, but, again, this rarely happens. In 2014, ISPOR established a Good Practices for Outcome Research Task Force to address the use of HSUs in cost-effectiveness models. This task force report provides recommendations for researchers who identify, review, and synthesize HSUs for use in cost-effectiveness models; analysts who use the results in models; and reviewers who critically appraise the suitability and validity of the HSUs selected for use in models. The associated Minimum Reporting Standards of Systematic Review of Utilities for Cost-Effectiveness checklist created by the task force provides criteria to judge the appropriateness of the HSUs selected for use in cost-effectiveness models and is suitable for use in different international settings.

A model for measuring the health burden of classic congenital adrenal hyperplasia in adults.

AIM: Patients with classic congenital adrenal hyperplasia (CAH) have poor health outcomes. In the absence of a comprehensive observational study, this manuscript provides a model to estimate the lifetime disease burden of adults with classic CAH. METHODS: The model, built in Excel, comprises subdomains addressing the health consequences of CAH and synthesises evidence from clinical and epidemiological studies on health outcomes. RESULTS: The model estimates that adults with classic CAH will implement 'sick day rules' (doubling or tripling glucocorticoid and/or use of parenteral therapy) 171 times over their lifetime and attend hospital for adrenal crisis on 11 occasions. In a population of 1000, over 200 will die of a condition complicated by adrenal crisis resulting, on average, in a loss of 7 years of life. Patients with CAH may also suffer from excess CVD events. Treatment with glucocorticoids almost doubles the risk of bone fractures in patients with CAH compared to the general population, leading on average to an additional 0·8 fractures per patient with CAH over their lifetime. CONCLUSIONS: The disease burden model highlights gaps in evidence, particularly regarding intensity of care and adrenal crisis, and the relationship between control of CAH and risks of CVD, osteoporosis, diabetes and infertility. The model can be used for research on the impact of new clinical pathways and therapeutic interventions in terms of clinical events and cost.

Are policy decisions on surgical procedures informed by robust economic evidence? A systematic review.

OBJECTIVES: The aim of this study was to examine the empirical and methodological cost-effectiveness evidence of surgical interventions for breast, colorectal, or prostate cancer. METHODS: A systematic search of seven databases including MEDLINE, EMBASE, and NHSEED, research registers, the NICE Web site and conference proceedings was conducted in April 2012. Study quality was assessed in terms of meeting essential, preferred and UK NICE specific requirements for economic evaluations. RESULTS: The seventeen (breast = 3, colorectal = 7, prostate = 7) included studies covered a broad range of settings (nine European; eight non-European) and six were published over 10 years ago. The populations, interventions and comparators were generally well defined. Very few studies were informed by literature reviews and few used synthesized clinical evidence. Although the interventions had potential differential effects on recurrence and mortality rates, some studies used relatively short time horizons. Univariate sensitivity analyses were reported in all studies but less than a third characterized all uncertainty with a probabilistic sensitivity analysis. Although a third of studies incorporated patients' health-related quality of life data, only four studies used social tariff values. CONCLUSIONS: There is a dearth of recent robust evidence describing the cost-effectiveness of surgical interventions in the management of breast, colorectal and prostate cancers. Many of the recent publications did not satisfy essential methodological requirements such as using clinical evidence informed by a systematic review and synthesis. Given the ratio of potential benefit and harms associated with cancer surgery and the volume of resources consumed by these, there is an urgent need to increase economic evaluations of these technologies.

Association between body mass index and health-related quality of life, and the impact of self-reported long-term conditions - cross-sectional study from the south Yorkshire cohort dataset.

BACKGROUND: We sought to quantify the relationship between body mass index (BMI) and health-related quality (HRQoL) of life, as measured by the EQ-5D, whilst controlling for potential confounders. In addition, we hypothesised that certain long-term conditions (LTCs), for which being overweight or obese is a known risk factor, may mediate the association between BMI and HRQoL. Hence the aim of our study was to explore the association between BMI and HRQoL, first controlling for confounders and then exploring the potential impact of LTCs. METHODS: We used baseline data from the South Yorkshire Cohort, a cross-sectional observational study which uses a cohort multiple randomised controlled trial design. For each EQ-5D health dimension we used logistic regression to model the probability of responding as having a problem for each of the five health dimensions. All continuous variables were modelled using fractional polynomials. We examined the impact on the coefficients for BMI of removing LTCs from our model. We considered the self-reported LTCs: diabetes, heart disease, stroke, cancer, osteoarthritis, breathing problems and high blood pressure. RESULTS: The dataset used in our analysis had data for 19,460 individuals, who had a mean EQ-5D score of 0.81 and a mean BMI of 26.3 kg/m². For each dimension, BMI and all of the LTCs were significant predictors. For overweight or obese individuals (BMI ≥ 25 kg/m²), each unit increase in BMI was associated with approximately a 3% increase in the odds of reporting a problem for the anxiety/depression dimension, a 8% increase for the mobility dimension, and approximately 6% for the remaining dimension s. Diabetes, heart disease, osteoarthritis and high blood pressure were identified as being potentially mediating variables for all of the dimensions. CONCLUSIONS: Compared to those of a normal weight (18.5 < BMI < 25 kg/m²), overweight and obese individuals had a reduced HRQoL, with each unit increase in BMI associated with approximately a 6% increase in the odds of reporting a problem on any of the EQ-5D health dimensions. There was evidence to suggest that diabetes, heart disease, osteoarthritis and high blood pressure may mediate the association between being overweight and HRQoL.

Principles of health economic evaluations of lipid-lowering strategies.

PURPOSE OF REVIEW: Policy decision-making in cardiovascular disease is increasingly informed by the results generated from decision-analytic models (DAMs). The methodological approaches and assumptions used in these DAMs impact on the results generated and can influence a policy decision based on a cost per quality-adjusted life year (QALY) threshold. Decision makers need to be provided with a clear understanding of the key sources of evidence and how they are used in the DAM to make an informed judgement on the quality and appropriateness of the results generated. RECENT FINDINGS: Our review identified 12 studies exploring the cost-effectiveness of pharmaceutical lipid-lowering interventions published since January 2010. All studies used Markov models with annual cycles to represent the long-term clinical pathway. Important differences in the model structures and evidence base used within the DAMs were identified. Whereas the reporting standards were reasonably good, there were many instances when reporting of methods could be improved, particularly relating to baseline risk levels, long-term benefit of treatment and health state utility values. SUMMARY: There is a scope for improvement in the reporting of evidence and modelling approaches used within DAMs to provide decision makers with a clearer understanding of the quality and validity of the results generated. This would be assisted by fuller publication of models, perhaps through detailed web appendices.

Comparing EQ-5D scores for comorbid health conditions estimated using 5 different methods.

BACKGROUND: There is currently no consensus on the most appropriate method to estimate health state utility values (HSUVs) for comorbid health conditions. OBJECTIVE: The objective of the study was to assess the accuracy by applying 5 different methods to an EQ-5D dataset. METHODS: EQ-5D data (n=41,174) from the Health Survey for England were used to compare HSUVs generated using the additive, multiplicative and minimum methods, the adjusted decrement estimator, and a linear regression. RESULTS: The additive and multiplicative methods underestimated the majority of HSUVs and the magnitude of the errors increased as the actual HSUV increased. Conversely, the minimum and adjusted decrement estimator methods overestimated the majority of HSUVs and the magnitude of errors increased as the actual HSUV decreased. Although the simple linear model produced the most accurate results, there was a tendency to underpredict higher HSUVs and overpredict lower HSUVs. The magnitude and direction of mean errors could be driven by the actual scores being estimated in addition to the technique used and the HSUVs estimated using an adjusted baseline were generally more accurate. CONCLUSIONS: The additive and minimum methods performed very poorly in our data. Although the simple linear model gave the most accurate results, the model requires validating in external data obtained from the EQ-5D and other preference-based measures. Based on the current evidence base, we would recommend the multiplicative method is used together with a range of univariate sensitivity analyses.

Using health state utility values in models exploring the cost-effectiveness of health technologies.

BACKGROUND: To improve comparability of economic data used in decision making, some agencies recommend that a particular instrument should be used to measure health state utility values (HSUVs) used in decision-analytic models. The methods used to incorporate HSUVs in models, however, are often methodologically poor and lack consistency. Inconsistencies in the methodologies used will produce discrepancies in results, undermining policy decisions informed by cost per quality-adjusted life-years. OBJECTIVE: To provide an overview of the current evidence base relating to populating decision-analytic models with HSUVs. FINDINGS: Research exploring suitable methods to accurately reflect the baseline or counterfactual HSUVs in decision-analytic models is limited, and while one study suggested that general population data may be appropriate, guidance in this area is poor. Literature describing the appropriateness of different methods used to estimate HSUVs for combined conditions is growing, but there is currently no consensus on the most appropriate methodology. While exploratory analyses suggest that a statistical regression model might improve accuracy in predicted values, the models require validation and testing in external data sets. Until additional research has been conducted in this area, the current evidence suggests that the multiplicative method is the most appropriate technique. Uncertainty in the HSUVs used in decision-analytic models is rarely fully characterized in decision-analytic models and is generally poorly reported. CONCLUSIONS: A substantial volume of research is required before definitive detailed evidence-based practical advice can be provided. As the methodologies used can make a substantial difference to the results generated from decision-analytic models, the differences and lack of clarity and guidance will continue to lead to inconsistencies in policy decision making.

Tails from the peak district: adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values.

OBJECTIVES: Health utility data generated by using the EuroQol five-dimensional (EQ-5D) questionnaire are right bounded at 1 with a substantial gap to the next set of observations, left bounded, and multimodal. These features present challenges to the estimation of the effect of clinical and socioeconomic characteristics on health utilities. Our objective was to develop and demonstrate an appropriate method for dealing with these features. METHODS: We developed a statistical model that incorporates an adjusted limited dependent variable approach to reflect the upper bound and the large gap in feasible EQ-5D questionnaire values. Further flexibility was then gained by adopting a mixture modeling framework to address the multimodality of the EQ-5D questionnaire distribution. We compared the performance of these approaches with that of those frequently adopted in the literature (linear and Tobit models) by using data from a clinical trial of patients with rheumatoid arthritis. RESULTS: We found that three latent classes are appropriate in estimating EQ-5D questionnaire values from function, pain, and sociodemographic factors. Superior performance of the adjusted limited dependent variable mixture model was achieved in terms of Akaike and Bayesian information criteria, root mean square error, and mean absolute error. Unlike other approaches, the adjusted limited dependent variable mixture model fits the data well at high EQ-5D questionnaire levels and cannot predict unfeasible EQ-5D questionnaire values. CONCLUSIONS: The distribution of the EQ-5D questionnaire is characterized by features that raise statistical challenges. It is well known that standard approaches do not perform well for this reason. This article developed an appropriate method to reflect these features by combining limited dependent variable and mixture modeling and demonstrated superior performance in a rheumatoid arthritis setting. Further refinement of the general framework and testing in other data sets are warranted. Analysis of utility data should apply methods that recognize the distributional features of the data.

Healthcare costs and productivity losses directly attributable to ankylosing spondylitis.

OBJECTIVES: To describe the healthcare resource use and productivity losses associated with patients with ankylosing spondylitis (AS) and explore the relationship between disease severity and total costs. METHODS: A cross-sectional postal survey was conducted on a sample of 1,000 patients with AS randomly selected from registries at 10 secondary care rheumatology centres in the UK. Information on demographic characteristics, disease and functional activity, healthcare use and work status (presenteeism and absenteeism) during the previous three months was collected. The relationship between disease severity and total costs was explored using a two-part regression model, controlling for age, gender and disease duration and validated on respondents (n=470) of the second round of the survey. RESULTS: Respondents at baseline (n=612) covered the full spectrum of AS, had a mean BASDAI of 4.6 and 55.3% of individuals scored at least 4 on the BASDAI scale. The mean (median) three month total cost was £2,802 (£1,160). Both physical function and disease activity were significant predictors of total costs. Mean (median) three month total costs for patients with BASDAI <4, 4-6 and >6 were £1,331 (£502), £2,790 (£1,281) and £4,840 (£5,017) respectively. Direct National Health Service funded healthcare costs contributed to just 15% of total costs while unemployment, absenteeism from work and reduced productivity at work accounted for 63.2%, 1.4% and 19.0% of total costs, respectively. CONCLUSIONS: This study shows that direct healthcare costs alone do not describe the total costs associated with AS and that productivity losses associated with AS are considerable.

Estimating health state utility values for comorbid health conditions using SF-6D data.

INTRODUCTION: When health state utility values for comorbid health conditions are not available, data from cohorts with single conditions are used to estimate scores. The methods used can produce very different results and there is currently no consensus on which is the most appropriate approach. OBJECTIVE: The objective of the current study was to compare the accuracy of five different methods within the same dataset. METHOD: Data collected during five Welsh Health Surveys were subgrouped by health status. Mean short-form 6 dimension (SF-6D) scores for cohorts with a specific health condition were used to estimate mean SF-6D scores for cohorts with comorbid conditions using the additive, multiplicative, and minimum methods, the adjusted decrement estimator (ADE), and a linear regression model. RESULTS: The mean SF-6D for subgroups with comorbid health conditions ranged from 0.4648 to 0.6068. The linear model produced the most accurate scores for the comorbid health conditions with 88% of values accurate to within the minimum important difference for the SF-6D. The additive and minimum methods underestimated or overestimated the actual SF-6D scores respectively. The multiplicative and ADE methods both underestimated the majority of scores. However, both methods performed better when estimating scores smaller than 0.50. Although the range in actual health state utility values (HSUVs) was relatively small, our data covered the lower end of the index and the majority of previous research has involved actual HSUVs at the upper end of possible ranges. CONCLUSIONS: Although the linear model gave the most accurate results in our data, additional research is required to validate our findings.

Using health state utility values from the general population to approximate baselines in decision analytic models when condition-specific data are not available.

BACKGROUND: Decision analytic models in health care require baseline health-related quality of life data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per quality adjusted life years thresholds. OBJECTIVE: The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition specific data are not available. METHODS: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. RESULTS: More than 45% of respondents (n = 41,174) reported at least one condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses, but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one condition. In these instances, if condition specific data are not available, data from respondents who report they do not have any prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on health-related quality of life may not be constant across ages for all conditions and these relationships may be condition specific. Additional research is required to validate our findings.

The CHD challenge: comparing four cost-effectiveness models.

OBJECTIVES: To compare four UK models evaluating the cost-effectiveness of interventions in coronary heart disease (CHD), exploring the relative importance of structure and inputs in accounting for differences, and the scope for consensus on structure and data. METHODS: We compared published cost-effectiveness results (incremental cost, quality-adjusted life year, and cost-effectiveness ratio) of three models conforming to the National Institute for Health and Clinical Excellence guidelines dealing with three interventions (statins, percutaneous coronary intervention, and clopidogrel) with a model developed in Southampton. Comparisons were made using three separate stages: 1) comparison of published results; 2) comparison of the results using the same data inputs wherever possible; and 3) an in-depth exploration of reasons for differences and the potential for consensus. RESULTS: Although published results differed by up to 73% (for statins), standardization of inputs (stage 2) narrowed these gaps. Greater understanding of the reasons for differences was achieved, but a consensus on preferred values for all data inputs was not reached. CONCLUSIONS: We found that published guidance on methods was important to reduce variation in important model inputs. Although the comparison of models did not lead to consensus for all model inputs, it provided a better understanding of the reasons for these differences, and enhanced the transparency and credibility of all models. Similar comparisons would be aided by fuller publication of models, perhaps through detailed web appendices.

Estimation of health care costs as a function of disease severity in people with psoriatic arthritis in the UK.

OBJECTIVES: The primary aim of this study was to estimate annual health care costs for biologic-naïve patients with PsA in the UK. The relationship between disease severity, defined by physical limitations, and costs was also explored. METHODS: This study utilized data from the British Society of Rheumatology Biologics Register (BSRBR) to develop a multivariate model estimating disease severity from parameters available in routine primary care data. The HAQ Disability Index was used to determine disease severity. This algorithm was then applied to routine data from The Health Improvement Network (THIN). Annual costs were estimated for drugs, contacts with a general practitioner and other health care professionals, tests, hospital outpatient attendances and inpatient admissions from a National Health Service perspective using official tariffs. The relationship between disease severity and health care costs was estimated using a generalized linear model. RESULTS: Three hundred and fifty-six cases with PsA were identified in the BSRBR and 4492 in THIN. Total mean annual health care costs ranged from £11 to £20 782 with a mean of £1446 (s.d. £1756). When costs were sub-grouped by the predicted HAQ score, the mean annual observed costs ranged from £548 per person for the least severely affected (HAQ ≤ 1.2) to £4832 for the most severely affected (HAQ > 2.6). Prescription costs and secondary care episodes accounted for more than a third of total care costs each (38 and 34%, respectively). When the relationship between disease severity and costs was examined, estimated HAQ was found to be a significant predictor of total health care costs. CONCLUSIONS: Treatment of people with PsA resulted in considerable financial costs and these costs varied markedly by disease severity.

Populating an economic model with health state utility values: moving toward better practice.

BACKGROUND: The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using baselines of perfect health are not comparable with those generated using baselines adjusted to reflect the HSUVs associated with the health condition. Despite this, there is no guidance on the preferred techniques and little research describing the effect on cost per quality adjusted life-year (QALY) results when using the different methods. METHODS: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health-state utility profiles (perfect health, no history of CVD, general population) in conjunction with models (minimum, additive, multiplicative) frequently used to approximate scores for health states with multiple health conditions. HSUVs are calculated using the EQ-5D UK preference-based algorithm. RESULTS: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with age, overestimating the benefits of treatment. The results generated using baselines from the general population are comparable to those obtained using baselines from individuals with no history of CVD. The minimum model biases results in favor of younger-aged cohorts. The additive and multiplicative models give similar results. CONCLUSION: Although further research in additional health conditions is required to support our findings, our results highlight the need for analysts to conform to an agreed reference case. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation.

Predicting the short form-6D preference-based index using the eight mean short form-36 health dimension scores: estimating preference-based health-related utilities when patient level data are not available.

OBJECTIVE: The objective is to derive an algorithm to predict a cohort preference-based short form-6D (short form-6D) score using the eight mean health dimension scores from the short form-36 (SF-36) when patient level data are not available. METHODS: Health-related quality of life data (N = 6890) covering a wide range of health conditions was used to explore the relationship between the SF-6D and the eight health dimension scores. Models obtained using ordinary least square regressions were compared for goodness of fit and predictive abilities on both within-sample subgroups and out-of-sample published data sets. RESULTS: The models explained more than 83% of the variance in the individual SF-6D scores with a mean absolute error of 0.040. When using mean health dimension scores from within-sample subgroups and out-of-sample published data sets, the majority of predicted scores were well within the minimal important difference (0.041) for the SF-6D. CONCLUSIONS: This article presents a mechanism to estimate a mean cohort preference-based SF-6D score using the eight mean health dimension scores of the SF-36. Using published summary statistics, the out-of-sample validation demonstrates that the algorithms can be used to inform both clinical and economic research. Further research is required in different health conditions.

Statin therapy in rheumatoid arthritis: a cost-effectiveness and value-of-information analysis.

HMG-CoA reductase inhibitors (statins) are potentially excellent candidate agents for patients with rheumatoid arthritis (RA). They reduce both cardiovascular risks and RA disease activity. To evaluate the potential long-term effects of statin therapy among patients with RA, and to determine their associated cost effectiveness by incorporating both the cardiovascular and the anti-rheumatic benefits. A Markov decision-analytic model was developed to simulate cardiovascular and RA disease profiles over time. The impact of statin therapy was estimated by adjusting the risk of coronary heart disease (CHD) events and changes in the RA Disease Activity Score (DAS28), based on the results of a randomized trial. The benefits (QALYs) and costs (in year 2005 values) were evaluated from a US payer perspective. A full uncertainty analysis, including a value-of-information (VOI) analysis, was undertaken to evaluate the importance of individual parameters. Using a 10-year time horizon, the additional cost and QALYs of statin therapy were estimated to be USD4690 and 0.44 QALYs, respectively, resulting in an incremental cost-effectiveness ratio (ICER) of USD10 650 per QALY (95% CI 1525, 156 565). The QALY gain associated with statin therapy depended more on the anti-rheumatic effects of statin therapy than on its cardiovascular prevention effect. The VOI analysis found the long-term benefit of statin therapy (i.e. >or=12 months) and the consequent impact on quality of life to be the most uncertain and, therefore, influential parameters. Our analysis indicates that the dual anti-inflammatory/cardiovascular benefits of statins could make this therapy highly cost effective in RA. However, uncertainties remain in the available data, warranting further research on refining the precise RA disease-activity benefits and health-utility changes associated with statin therapy, at least over a 12-month period.

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.

Estimating the health benefits and costs associated with ezetimibe coadministered with statin therapy compared with higher dose statin monotherapy in patients with established cardiovascular disease: results of a Markov model for UK costs using data registries.

BACKGROUND: Ezetimibe has been reported to improve lipid control in patients with established cardiovascular disease (CVD). OBJECTIVE: The aim of this study was to estimate the potential long-term impact on health status of prescribing ezetimibe in combination with statin therapy in patients with established CVD and evaluate its cost-effectiveness in a health economic model. METHODS: A Markov model was used to compare ezetimibe and statin combination therapy with statin monotherapy. A published relationship linking changes in low-density lipoprotein cholesterol and cardiovascular events was used to estimate the cardiovascular events avoided through lipid-lowering therapies. The model was populated using results of extensive literature searches and a meta-analysis of clinical evidence. An adjustment was applied to model second-line lipid-lowering benefits. Conservative assumptions were used to extend the patient pathway beyond the clinical evidence. The analysis took the perspective of the UK Department of Health; therefore, only direct costs were included. Costs were calculated as year-2006 British pounds. RESULTS: For a cohort of 1,000 hypothetical male patients aged 55 years, ezetimibe coadministered with current statin therapy was estimated to prevent a mean of 43 nonfatal myocardial infarctions, 7 nonfatal strokes, and 26 cardiovascular deaths over a lifetime, compared with doubling the current statin dose. The events avoided would provide a mean of 134 additional quality-adjusted life-years (QALYs). With a mean incremental cost of pound 3,693,000, the lifetime discounted cost per QALY gained would be pound 27,475 (95% CI, pound 27,331- pound 27,620) and would rise to pound 32,000 for men aged 75 years. CONCLUSIONS: The results suggest that, in some instances, ezetimibe coadministration may be cost-effective compared with statin monotherapy, but there are several limitations with this model. The economic effects of ezetimibe must be revisited when long-term effectiveness and safety data become available.

Deriving an algorithm to convert the eight mean SF-36 dimension scores into a mean EQ-5D preference-based score from published studies (where patient level data are not available).

OBJECTIVE: The objective of the study was to derive a method to predict a mean cohort EQ-5D preference-based index score using published mean statistics of the eight dimension scores describing the SF-36 health profile. METHODS: Ordinary least square regressions models are derived using patient level data (n = 6350) collected during 12 clinical studies. The models were compared for goodness of fit using standard techniques such as variance explained, the magnitude of errors in predicted values, and the proportion of values within the minimal important difference of the EQ-5D. Predictive abilities were also compared using summary statistics from both within-sample subgroups and published studies. RESULTS: The models obtained explained more than 56% of the variance in the EQ-5D scores. The mean predicted EQ-5D score was correct to within two decimal places for all models and the absolute error for the individual predicted values was approximately 0.13. Using summary statistics to predict within-sample subgroup mean EQ-5D scores, the mean errors (mean absolute errors) ranged from 0.021 to 0.077 (0.045-0.083). These statistics for the out-of-sample published data sets ranged from 0.048 to 0.099 (0.064-0.010). CONCLUSIONS: The models provided researchers with a mechanism to estimate EQ-5D utility data from published mean dimension scores. This research is unique in that it uses mean statistics from published studies to validate the results. While further research is required to validate the results in additional health conditions, the algorithms can be used to derive additional preference-based measures for use in economic analyses.