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Measuring preferences for cost-utility analysis: how choice of method may influence decision-making
Christine M McDonough1, Anna N A Tosteson
1Dartmouth Medical School, Multidisciplinary Clinical Research Center in Musculoskeletal Diseases, Lebanon, New Hampshire 03756, USA.
Abstract:
Preferences for health are required when the economic value of healthcare interventions are assessed within the framework of cost-utility analysis. The objective of this paper was to review alternative methods for preference measurement and to evaluate the extent to which the method may affect healthcare decision-making. Two broad approaches to preference measurement that provide societal health state values were considered: (i) direct measurement; and (ii) preference-based health state classification systems. Among studies that compared alternative preference-based systems, the EQ-5D tended to provide larger change scores and more favourable cost-effectiveness ratios than the Health Utilities Index (HUI)-2 and -3, while the SF-6D provided smaller change scores and less favourable ratios than the other systems. However, these patterns may not hold for all applications. Empirical evidence comparing systems and decision-making impact suggests that preferences will have the greatest impact on economic analyses when chronic conditions or long-term sequelae are involved. At present, there is no clearly superior method, and further study of cost-effectiveness ratios from alternative systems is needed to evaluate system performance. Although there is some evidence that incremental cost-effectiveness ratio (ICER) thresholds (e.g. $US50,000 per QALY gained) are used in decision-making, they are not strictly applied. Nonetheless, as ICERs rise, the probability of acceptance of a new therapy is likely to decrease, making the differences in QALYs obtained using alternative methods potentially meaningful. It is imperative that those conducting cost-utility analyses characterise the impact that uncertainty in health state values has on the economic value of the interventions studied. Consistent reporting of such analyses would provide further insight into the policy implications of preference measurement.
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