Weighting must wait: incorporating equity concerns into cost effectiveness analysis may take longer than expected

Current practice in economic evaluation is to assign equal social value to a unit of health improvement (“a QALY is a QALY is a QALY”). Alternative views of equity are typically considered separately to efficiency. One proposal seeks to integrate these two sets of societal concerns by attaching equity weights to QALYs. To date, research in pursuit of this goal has focussed on candidate equity criteria and methods for estimating such weights. It has implicitly been assumed that should legitimate, valid, and reliable equity weights become available, it would be a straightforward task to incorporate them into as a separate simple calculation after estimating cost per unweighted QALY. This paper suggests that in many situations these simple approaches to incorporating equity weights will not appropriately reflect the preferences on which the weights are based and therefore equity weights must be incorporated directly into the cost effectiveness analysis. In addition to these technical issues, there are a number of practical challenges that arise from the movement from implicit to explicit consideration of equity. Equity weights should be incorporated in economic evaluation, but not until these challenges have been appropriately addressed

Weighting must wait: incorporating equity concerns into cost effectiveness analysis may take longer than expected

Current practice in economic evaluation is to assign equal social value to a unit of health improvement (“a QALY is a QALY is a QALY”). Alternative views of equity are typically considered separately to efficiency. One proposal seeks to integrate these two sets of societal concerns by attaching equity weights to QALYs. To date, research in pursuit of this goal has focussed on candidate equity criteria and methods for estimating such weights. It has implicitly been assumed that should legitimate, valid, and reliable equity weights become available, it would be a straightforward task to incorporate them into as a separate simple calculation after estimating cost per unweighted QALY. This paper suggests that in many situations these simple approaches to incorporating equity weights will not appropriately reflect the preferences on which the weights are based and therefore equity weights must be incorporated directly into the cost effectiveness analysis. In addition to these technical issues, there are a number of practical challenges that arise from the movement from implicit to explicit consideration of equity. Equity weights should be incorporated in economic evaluation, but not until these challenges have been appropriately addressed

Multilevel modelling of cost data: an application to thrombolysis and primary angioplasty in the UK NHS

Cost data are frequently collected from several locations and tend to be non negative and skewed. Generalised linear multilevel models provide a means of dealing with each of these issues. This paper compares several statistical models within this class using data drawn from an observational study of 3,000 patients treated for heart attack in 15 UK NHS hospitals. A number of alternative link functions and covariates were considered. We demonstrate that whilst it is important to take account of clustering in the data, the precise manner in which this is done is equally important. Models which allow for correlation between the random effects components and heteroskedasticity across all hospitals performed best in terms of model fit and made substantial di¤erences to cost estimates

Weighting must wait: incorporating equity concerns into cost effectiveness analysis may take longer than expected

Current practice in economic evaluation is to assign equal social value to a unit of health improvement (“a QALY is a QALY is a QALY”). Alternative views of equity are typically considered separately to efficiency. One proposal seeks to integrate these two sets of societal concerns by attaching equity weights to QALYs. To date, research in pursuit of this goal has focussed on candidate equity criteria and methods for estimating such weights. It has implicitly been assumed that should legitimate, valid, and reliable equity weights become available, it would be a straightforward task to incorporate them into as a separate simple calculation after estimating cost per unweighted QALY. This paper suggests that in many situations these simple approaches to incorporating equity weights will not appropriately reflect the preferences on which the weights are based and therefore equity weights must be incorporated directly into the cost effectiveness analysis. In addition to these technical issues, there are a number of practical challenges that arise from the movement from implicit to explicit consideration of equity. Equity weights should be incorporated in economic evaluation, but not until these challenges have been appropriately addressed

Efficiency, equity, and NICE clinical guidelines

The stated purpose of clinical guidelines from the United Kingdom's National Institute for Clinical Excellence (NICE) is to "help healthcare professionals and patients make the right decisions about healthcare in specific clinical circumstances." However, what constitutes "the right decisions" depends on your point of view. For individual patients the right decision is that which maximises their wellbeing, and this is properly the concern of the clinician. Yet in resource constrained healthcare systems this will not always coincide with the right decisions for patients in general or society as a whole, thereby leading to some understandable tensions. NICE is a national policy making body whose responsibility is clearly broader than the individual patient. This wider viewpoint is reflected in NICE's technology appraisals by the central role afforded to cost effectiveness. We argue that the methods currently used by the NICE clinical guideline programme confuse these two viewpoints

The relationship between EQ-5D, HAQ and pain in patients with rheumatoid arthritis: further validation and development of the limited dependent variable, mixture model approach

Objective To provide robust estimates of EQ-5D as a function of the Health Assessment Questionnaire (HAQ) and pain in patients with rheumatoid arthritis. Method Repeated observations of patients diagnosed with RA in a US observational cohort (n=100,398 observations) who provided data on HAQ, pain on a visual analogue scale and the EQ-5D questionnaire. We use a bespoke mixture modelling approach to appropriately reflect the characteristics of the EQ-5D instrument and compare this to results from linear regression. Results The addition of pain alongside HAQ as an explanatory variable substantially improves explanatory power. The preferred model is a four component mixture. Unlike the linear regression it exhibits very good fit to the data, does not suffer from problems of bias or predict values outside the feasible range. Conclusions It is appropriate to model the relationship between HAQ and EQ-5D but only if suitable statistical methods are applied. Linear models underestimate the QALY benefits, and therefore the cost effectiveness, of therapies. The bespoke mixture model approach outlined here overcomes this problem. The addition of pain as an explanatory variable greatly improves the estimates

A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes

Background: Analysts often need to estimate health state utility values as a function of other outcome measures. Utility values like EQ-5D have several unusual characteristics that make standard statistical methods inappropriate. We have developed a bespoke approach based on mixture models to directly estimate EQ-5D. An indirect method, “response mapping”, first estimates the level on each of the five dimensions of the EQ-5D descriptive system and then calculates the expected tariff score. These methods have never previously been compared. Methods: We use a large observational database of patients diagnosed with Rheumatoid Arthritis (n=100,398 observations). Direct estimation of UK EQ-5D scores as a function of Health Assessment Questionnaire (HAQ), pain and age was performed using a limited dependent variable mixture model. Indirect modelling was undertaken using a set of generalized ordered probit models with expected tariff scores calculated mathematically. Linear regression was reported for comparison purposes. Results: The linear model fits poorly, particularly at the extremes of the distribution. Both the bespoke mixture model and the generalized ordered probit approach offer improvements in fit over the entire range of EQ-5D. Mean average error is 10% and 5% lower compared to the linear model respectively. Root mean squared error is 3% and 2% lower. The mixture model demonstrates superior performance to the indirect method across almost the entire range of pain and HAQ. Limitations: There is limited data from patients in the most extreme HAQ health states. Conclusions: Modelling of EQ-5D from clinical measures is best performed directly using the bespoke mixture model. This substantially outperforms the indirect method in this example. Linear models are inappropriate, suffer from systematic bias and generate values outside the feasible range

Modelling the relationship between the WOMAC osteoarthritis index and EQ-5D

Objective Economic evaluation typically is conducted using health state utilities to estimate treatment benefits. However, such outcomes are often missing from studies of clinical effectiveness. This study aims to bridge that gap by providing appropriate methods to link values from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) to the EQ-5D utility instrument. Method Patients from a large registry of Spanish patients (n = 7072 observations) with knee or hip osteoarthritis who completed both WOMAC and EQ-5D was used. A mixture model approach was used based on distributions bespoke to the EQ-5D UK value set to estimate EQ-5D as a function of WOMAC pain, stiffness and function subscores. Results A five class mixture model provides very close fit to the observed data at all levels of disease severity. The overall mean (0.542 vs 0.542), median (0.620 vs 0.636) and the percentage of observations at full health (15 vs 14.8) were very similar between the observed data and the estimated model respectively. Stiffness has limited relationship to EQ-5D, whereas functional disability and pain are strong predictors. Conclusion EQ-5D can be reliably estimated from WOMAC subscale scores without any systematic bias using these results

Development of methods for the mapping of utilities using mixture models: An application to asthma

Objectives: To develop methods for mapping to preference-based measures using mixture model approaches. These methods are compared to map from the Asthma Quality of Life Questionnaire (AQLQ) to both EQ5D-5L and HUI-3 as the target health utility measures in an international dataset. Methods: Data from 856 patients with asthma collected as part of the Multi-Instrument Comparison (MIC) international project were used. Adjusted limited dependent variable mixture models (ALDVMMs) and beta-regression based mixture models were estimated. Optional inclusion of the gap between full health and the next value, and a mass point at the next feasible value were explored. Response-mapping could not be implemented due to missing data. Results: In all cases, model specifications which formally modelled the gap between full health and the next value were an improvement on those which did not. Mapping to HUI3 required more components in the mixture models than mapping to EQ5D-5L due to its uneven distribution. The optimal beta-based mixture models mapping to HUI3 included a probability mass at the utility value adjacent to full health. This is not the case when estimating EQ5D-5L, due to the low proportion of observations at this point. Conclusion: The beta-based mixture models marginally outperformed ALDVMM in this dataset when comparing models with the same number of components. This is at the expense of requiring a larger number of parameters and estimation time. Both model types are able to closely fit the data without biased characteristic of many mapping approaches. Skilled judgment is critical in determining the optimal model. Caution is required in ensuring a truly global maximum likelihood has been identified