From a different angle: A novel approach to health valuation

The value of a health state is typically described relative to the value of an optimal state, specifically as a ratio ranging from unity (equal to optimal health) to negative infinity. Incorporating potentially infinite values is a challenging issue in the econometrics of health valuation. In this paper, we apply a directional statistics approach based on the assumption of wavering preference. Unlike ratio statistics, directional statistics are based on polar coordinates (angle, radius). The range of angles is bounded between 45 degrees (unity) and negative 90 degrees (i.e., negative infinity); therefore, mean angles are well behaved and negate the impetus behind arbitrary data manipulations. Using time trade-off (TTO) responses from the seminal Measurement and Valuation of Health study, we estimate 243 EQ-5D health state values by minimizing circular variance with and without radial weights. For states with published values greater than zero (i.e., better-than-death), the radially weighted estimates are nearly identical to the published values (Mean Absolute Difference 0.07; Lin’s rho 0.94). For worse-than-death states, the estimates are substantially lower than the published values (Mean Absolute Difference 0.186; Lin’s rho 0.576). For the worst EQ-5D state (33333), the published value is -0.59 and the directional estimate is -1.11. By taking a directional statistics approach, we circumvent problems inherent to ratio statistics and the systematic bias introduced by arbitrary data manipulations. The predictions suggest that published estimates overvalue severe states. This paper examines TTO responses; however, it may be extended to all forms of health valuation

Mathematical Approaches in Economic Evaluations: Applying techniques from different disciplines

Health economics (HE) is a multi-disciplinary field with links to economics, psychology and medicine. This is especially apparent in economic evaluations (EE) which have become an integral part in the management of health care systems in many western countries. In economic evaluations, information on a disease, on the cost of a treatment and on the effectiveness of the treatment is combined into a single mathematical model. This model is then used to assess the cost effectiveness of a treatment for the disease. The mathematical techniques employed to obtain and describe the information originate from three distinct mathematical disciplines associated with economics, psychology and medicine: econometrics, psychometrics and (bio)statistics. Even though there is a large amount of overlap, they all originated as separate disciplines and were developed with different perspectives in mind. This means that researchers in HE have a wide variety of different statistical and mathematical techniques at their disposal. This dissertation shows how ideas and approaches from different disciplines can be applied in solving health economic problems. Basic statistical techniques common to all fields, such as linear regression, are common. They are applied in most of the studies presented in this thesis. In addition to this, the studies described in this thesis show how more specialised techniques and approaches can be used outside the field where they were originally developed. In particular they are used in economic evaluations and the measurement and valuation of health related quality of life

Valuation and modeling of EQ-5D-5L health states using a hybrid approach

Background: The EQ-5D instrument is the most widely used preference-based health-related quality of life questionnaire in cost-effectiveness analysis of health care technologies. Recently, a version called EQ-5D-5L with 5 levels on each dimension was developed. This manuscript explores the performance of a hybrid approach for the modeling of EQ-5D-5L valuation data. Methods: Two elicitation techniques, the composite time trade-off, and discrete choice experiments, were applied to a sample of the Spanish population (n=1000) using a computer-based questionnaire. The sampling process consisted of 2 stages: stratified sampling of geographic area, followed by systematic sampling in each area. A hybrid regression model combining composite time trade-off and discrete choice data was used to estimate the potential value sets using main effects as starting point. The comparison between the models was performed using the criteria of logical consistency, goodness of fit, and parsimony. Results: Twenty-seven participants from the 1000 were removed following the exclusion criteria. The best-fitted model included 2 significant interaction terms but resulted in marginal improvements in model fit compared to the main effects model. We therefore selected the model results with main effects as a potential value set for this methodological study, based on the parsimony criteria. The results showed that the main effects hybrid model was consistent, with a range of utility values between 1 and -0.224. Conclusion: This paper shows the feasibility of using a hybrid approach to estimate a value set for EQ-5D-5L valuation data.</p

The Better than Dead Method: Feasibility and Interpretation of a Valuation Study

Background Traditionally, the valuation of health states worse than being dead suffers from two problems: [1] the use of different elicitation methods for positive and negative values, necessitating arbitrary transformations to map negative to positive values; and [2] the inability to quantify that values are time dependent. The Better than Dead (BTD) method is a health-state valuation method where states with a certain duration are compared with being dead. It has the potential to overcome these problems. Objectives To test the feasibility of the BTD method to estimate values for the EQ-5D system. Methods A representative sample of 291 Dutch respondents (aged 18-45 years) was recruited. In a web-based questionnaire, preferences were elicited for a selection of 50 different health states with six durations between 1 and 40 years. Random-effects models were used to estimate the effects of socio-demographic and experimental variables, and to estimate values for the EQ-5D. Test-retest reliability was assessed in 41 respondents. Results Important determinants for BTD were a religious life stance [odds ratio 4.09 (2.00-8.36)] and the educational level. The fastest respondents more often preferred health-state scenarios to being dead and had lower test-retest reliability (0.45 versus 0.77 and 0.84 for fast, medium and slow response times, respectively). The results showed a small number of so-called maximal endurable time states. Conclusion Valuating health states using the BTD method is feasible and reliable. Further research should explore how the experimental setting modifies how values depend on time

Mix and match. A simulation study on the impact of mixed-Treatment comparison methods on health-economic outcomes

Background Decision-Analytic cost-effectiveness (CE) models combine many parameters, often obtained after meta-Analysis. Aim We compared different methods of mixed-Treatment comparison (MTC) to combine transition and event probabilities derived from several trials, especially with respect to health-economic (HE) outcomes like (quality adjusted) life years and costs. Methods Trials were drawn from a simulated reference population, comparing two of four fictitious interventions. The goal was to estimate the CE between two of these. The amount of heterogeneity between trials was varied in scenarios. Parameter estimates were combined using direct comparison, MTC methods proposed by Song and Puhan, and Bayesian generalized linear fixed effects (GLMFE) and random effects models (GLMRE). Parameters were entered into a Markov model. Parameters and HE outcomes were compare

Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations

__Background:__ Discrete choice experiments (DCEs) are increasingly used for health state valuations. However, the values derived from initial DCE studies vary widely. We hypothesize that these findings indicate the presence of unknown sources of bias that must be recognized and minimized. Against this background, we studied whether values derived from a DCE are sensitive to how well the DCE design spans the severity range. __Methods:__ We constructed an experiment involving three variants of DCE tasks for health state valuation: standard DCE, DCE-death, and DCE-duration. For each type of DCE, an experimental design was generated under two different conditions, enabling a comparison of health state values derived from current best practice Bayesian efficient DCE designs with values derived from ‘severity-stratified’ designs that control for coverage of the severity range in health state selection. About 3000 respondents participated in the study and were randomly assigned to one of the six study arms. __Results:__ Imposing the severity-stratified restriction had a large effect on health states sampled for the DCE-duration approach. The unstratified efficient design returned a skewed distribution of selected health states, and this introduced bias. The choice probability of bad health states was underestimated, and time trade-offs to avoid bad states were overestimated, resulting in too low values. Imposing the same restriction had limited effect in the DCE-death approach and standard DCE. __Conclusion:__ Variation in DCE-derived values can be partially explained by differences in how well selected health states spanned the severity range. Imposing a ‘severity stratification’ on DCE-duration designs is a validity requirement

Exploring the Impact of Adding a Respiratory Dimension to the EQ-5D-5L.

Objectives. To evaluate the impact of adding a respiratory dimension (a bolt-on dimension) to the EQ-5D-5L health state valuations. Methods. Based on extensive regression and principal component analyses, 2 respiratory bolt-on candidates were formulated: R1, limitations in physical activities due to shortness of breath, and R2, breathing problems. Valuation interviews for the selected bol

Quality Control Process for EQ-5D-5L Valuation Studies

Background: The values of the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) are elicited using composite time trade-off and discrete choice experiments. Unfortunately, data quality issues and interviewer effects were observed in the first few EQ-5D-5L valuation studies. To prevent these issues from occurring in later studies, the EuroQol Group established a cyclic quality control (QC) process. Objectives: To describe this QC process and show its impact on data quality. Methods: A newly developed QC tool provided information about protocol compliance, interviewer effects, and mean values by health state severity. In a cyclic process, this information is initially used to evaluate whether new interviewers meet minimal quality requirements and later to provide feedback about how their performance may be improved. To investigate the impact of this cyclic process, we compared the quality of the data in Dutch and Spanish valuation studies that did not have this QC process with that in the follow-up studies in the same countries that used the QC process. Data quality was measured using protocol violations, variability between interviewers, the proportion of inconsistent responders, and clustering of composite time trade-off values. Results: In Spain, protocol violations were reduced from 87% in the valuation study to 5% in the follow-up study and in the Netherlands from 20% to 8%. In both countries, interviewers performed more homogeneously in the follow-up studies. The number of inconsistent respondents was reduced by 23.2% in Spain and 23.6% in the Netherlands. Values were less clustered in the follow-up studies. Conclusions: The implementation of a strict QC process in EQ-5D-5L valuation studies increases interviewer protocol compliance and promotes data quality