HHe ea al lt th h E Ec co on no om mi ic cs s a an nd d D De ec ci is si io on n S Sc ci ie en nc ce e D Di is sc cu us ss si io on n P Pa ap pe er r S Se er ri ie es s No. 08/02A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures INTRODUCTIONA common approach to assessing the outcomes of health care is to obtain patient reported descriptions of health status across various dimensions and then to apply a standardised numerical scoring system. There are many different measures of health, including several hundred condition specific measures of health designed for use in specific medical conditions or groups of condition (Spilker et al, 1990), and a number of generic measures designed to cover the core dimensions of health that are relevant across all medical conditions. Health measures can also be distinguished in terms of whether they generate a profile of dimension scores or a single index and if they produce a single index, whether or not the index has been derived using simple summation of item scores or by using preference weights obtained from patients or the general public (known as preference-based measures or multi-attribute utility scales).Patient reported measures of health have become widely used in clinical trials as primary or secondary outcomes. There is little agreement on which specific instruments should be used for this purpose. For assessing clinical efficacy, there is disagreement on whether to use a generic or condition specific measure, and between condition specific measures there is often disagreement amongst clinical researchers on the most appropriate instrument. As a result clinical trials around the world often use different measures for the same patient groups. This presents a substantial barrier to the synthesis of evidence.Preference-based measures of health are necessary to generate the health state utility values required to calculate QALYs for assessing the cost effectiveness of interventions.These are usually based on generic instruments (e.g. EQ-5D) that permit comparisons between patient groups, though there are examples of condition specific preference-based measures . Even for assessing clinical effectiveness, it could be argued that a preference-based index is necessary to deal with trade-offs made between outcomes. There has been a debate amongst health economists about the most appropriate preference-based generic measure to use in cost effectiveness analyses. Whilst the EQ-5D is the most widely applied in recent years (Brooks et al, 1996), the HUI3 (Feeny et al, 3 2002), QWB (Kaplan and Andersen, 1988), SF-6D (Brazier et al, 2002) and others continue to be used. However, different preference-based measures have been shown to generate different values on the same sample of patients (Marra et al, 2005; Feeny et al, 2004;Barton et al, 2004). Furthermore, many key clinical trials on the efficacy of new interventions do not have a generic measure and the recent FDA Guidance on using Patient Rep...

This project was funded by the UK Medical Research Council (MRC) as part of the MRC-NIHR methodology research programme (reference G0901486) and will be published in full in Health Technology Assessment; Vol. 18, No. 9. See the NIHR Journals Library website for further project information.

Quality-adjusted life-years (QALYs) are widely used as an outcome for the economic evaluation of health interventions. However, preference-based measures used to obtain health-related utility values to produce QALY estimates are not always included in key clinical studies. Furthermore, organizations responsible for reviewing or producing health technology assessments (HTAs) may have preferred instruments for obtaining utility estimates for QALY calculations. Where data using a preference-based measure or the preferred instrument have not been collected, it may be possible to "map" or "crosswalk" from other measures of health outcomes. The aims of this study were 1) to provide an overview of how mapping is currently used as reported in the published literature and in an HTA policy-making context, specifically at the National Institute for Health and Clinical Excellence in the United Kingdom, and 2) to comment on best current practice on the use of mapping for HTA more generally. The review of the National Institute for Health and Clinical Excellence guidance found that mapping has been used since first established but that reporting of the models used to map has been poor. Recommendations for mapping in HTA include an explicit consideration of the generalizability of the mapping function to the target sample, reporting of standard econometric and statistical tests including the degree of error in the mapping model across subsets of the range of utility values, and validation of the model(s). Mapping can provide a route for linking outcomes data collected in a trial or observational study to the specific preferred instrument for obtaining utility values. In most cases, however, it is still advantageous to directly collect data by using the preferred utility-based instrument and mapping should usually be viewed as a "second-best" solution.

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Aims The EORTC QLQ-C30 is one of the most commonly used measures in cancer but in its current form cannot be used in economic evaluation as it does not incorporate preferences. We address this gap by estimating a preference-based single index for cancer from the EORTC QLQ-C30 for use in economic evaluation. Methods Factor analysis, Rasch analysis and other psychometric analyses were undertaken on a clinical trial dataset of 655 patients with Multiple Myeloma to derive a health state classification from the QLQ-C30 that is amenable to valuation. A valuation study was conducted of 350 members of the UK general population using ranking and time trade-off. A series of regression models were fitted to the data, including the episodic random utility model (RUM) to derive preference weights for the classification system. Results The resulting health state classification system has 8 dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue and sleep disturbance, nausea, and constipation and diarrhoea) with 4 or 5 levels each. Mean and individual level additive multivariate regression models were estimated and compared. Mean absolute error ranges from 0.050 to 0.054 with no systematic errors. All models have few inconsistencies (0 to 2) in estimated preference weights. Conclusions It is feasible to derive a preference-based measure from the EORTC QLQ-C30 for use in economic evaluation, but this work needs to be extended to other countries and replicated across other conditions.