Roberta Ara

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.

Ezetimibe monotherapy for cholesterol lowering in 2,722 people: systematic review and meta-analysis of randomized controlled trials

Objectives.  To study the evidence on the efficacy and safety of ezetimibe monotherapy for the treatment of primary (heterozygous familial and non‐familial) hypercholesterolaemia.

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.

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.

An Overview of Economic Evaluations for Drugs Used in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, progressive, inflammatory disease that affects approximately 0.5–1% of the adult population. The introduction of new disease-modifying antirheumatic drugs (DMARDs) such as leflunomide, anakinra and the tumour necrosis factor (TNF)-α antagonists (infliximab, etanercept and adalimumab) have transformed the management of RA. In particular, the last class of agents has generated substantial controversy. Costing between

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

US16 000 and

Early high-dose lipid-lowering therapy to avoid cardiac events: a systematic review and economic evaluation

US20 000 per patient-year (2001 values), the potential greater efficacy of treatment with TNFα antagonists comes at much higher drug costs, making these agents natural candidates for cost-effectiveness analyses (CEAs).A MEDLINE search (until 31 January 2004) identified six original CEAs evaluating TNFα antagonists in RA. The aim of a CEA is to facilitate the allocation of scarce health resources and to inform policy decisions. However, to enhance the reliability and relevance of these analyses to policy makers, there must be similarity between the methodologies used. Recently, the OMERACT (Outcome Measures in Rheumatoid Arthritis Clinical Trials) group produced a document to define such a reference case; the OMERACT document was used as a foundation to structure comparisons and highlight discrepancies.The methodologies employed in each analysis differed; in particular, disparate time horizons, comparators, quantities of drug and treatment sequences prohibit the comparison of cost effectiveness between studies. Outcomes also differed between the analyses. Most reported health-related quality of life (HR-QOL) in quality-adjusted life-years (QALYs). The QALYs metric was based on preference scores that were typically derived from linear regressions using the Health Assessment Questionnaire (HAQ). However, models also used American College of Rheumatology (ACR) criteria, as well as the disease activity score (DAS). Common to all studies was the lack of data from long-term randomised studies where efficacy and resource consumption in comparison with standard care has been investigated. As such, investigators combined short-term randomised control trial data with that of a long-term observational cohort, and modelled cost effectiveness over an appropriate time horizon. In addition, most analyses lacked rigorous sensitivity analysis to examine the impact of uncertainty in the parameters.Those analyses that examined time horizons of 6 months and 1 year published incremental cost-effectiveness ratios (ICERs) of

What is the clinical effectiveness and cost-effectiveness of using drugs in treating obese patients in primary care? A systematic review.

US34 800 per ACR 70% response criteria (ACR70) weighted response (duration 6 months, 1999 values) and

Using Health State Utility Values from the General Population to Approximate Baselines in Decision Analytic Models when Condition-Specific Data are Not Available

US96 166 (duration 1 year, 2002 values). Analyses that modelled costs and health outcomes beyond the first year reported ICER estimates ranging between

Economic Evaluations in Rheumatoid Arthritis: A Critical Review of Measures Used to Define Health States

US26 800 (patients’ lifetime, 1998 values) and