Common Scale Valuations across Different Preference-Based Measures: Estimation Using Rank Data

Background. Different preference-based measures (PBMs) used to estimate quality-adjusted life years (QALYs) provide different utility values for the same patient. Differences are expected since values have been obtained using different samples, valuation techniques, and descriptive systems. Previous studies have estimated the relationship between pairs of PBMs using patient self-reported data. However, there is a need for an approach capable of generating values directly on a common scale for a range of PBMs using the same sample of general population respondents and valuation technique but keeping the advantages of the different descriptive systems. Methods. General public survey data (n = 501) in which respondents ranked health states described using subsets of 6 PBMs were analyzed. We develop a new model based on the mixed logit to overcome 2 key limitations of the standard rank-ordered logit modelnamely, the unrealistic choice pattern (independence of irrelevant alternatives) and the independence of repeated observations. Results. There are substantial differences in the estimated parameters between the 2 models (mean difference 0.07), leading to different orderings across the measures. Estimated values for the best states described by different PBMs are substantially and significantly different using the standard model, unlike our approach, which yields more consistent results. Limitations. Data come from an exploratory study that is relatively small both in sample size and coverage of health states. Conclusions. This study develops a new, flexible econometric model specifically designed to reflect appropriately the features of rank data. Results support the view that the standard model is not appropriate in this setting and will yield very different and apparently inconsistent results. PBMs can be compared using a common scale by implementation of this new approach.