Valuing states from multiple measures on the same visual analogue sale: a feasibility study

Previous methods of empirical mapping involve using regressions on patient or general population self-reported data from datasets involving two or more measures. This approach relies on overlap in the descriptive systems of the measures and assumes it is appropriate to use different measures on the same population, which may not always be the case. This paper presents a feasibility study for a new approach to mapping between preference-based measures (PBM) using general population visual analogue scale (VAS) values as a common yardstick. We use data from a valuation study of 502 members of the UK general population, where, using ranking and VAS tasks, interviewees simultaneously valued health states defined by three of six PBM: EQ-5D (generic), SF-6D (generic), HUI2 (generic for children and adults), AQL-5D (asthma specific), OPUS (social care specific) and ICECAP (capabilities). Regression techniques are used to estimate the relationship between these VAS values and the original value set (i.e. 'tariff'). These results are subsequently used to estimate the relationship between all six PBM to enable 'value-based mapping' between measures. This new method of mapping potentially has a useful role in evidence synthesis and cross programme comparisons in studies using different measures.