Valuing health using visual analogue scales and rank data: does the visual analogue scale contain cardinal information?

Valuation studies have favoured standard gamble (SG) and time trade off techniques (TTO) over visual analogue scales (VAS). The lack of observable trade off properties of VAS precludes preferences being measured on a cardinal scale. The inferences, and indeed the premise, of many perceived VAS deficiencies have been debated and, as part of this on-going debate, Brazier and McCabe (2007) asked whether VAS data added anything to rank data; echoing the suggestion that VAS functioned primarily as a prop for ranking exercises (Torrance et al 2001). Previous studies suggest that analyses of ordinal (rank data) can provide valuation functions broadly equivalent to cardinal health state data models (Salomon, 2003; McCabe et al, 2006). This paper adds to the debate by considering whether ordinal preferences, cardinal differences, and cardinal scores from VAS data provide substantively different valuation algorithms. That is, is potentially valuable information lost by using ordinal (rank) data rather than cardinal VAS scores? In the case where a cardinal difference model can provide a substantively better fit than an ordinal preference model, then we can say that the VAS contains useful cardinal information that cannot be incorporated into an ordinal model. A further aim is to assess whether VAS-based ordinal preferences (rather than TTO- or SG-based ordinal preferences) are likely to be sufficient reliably to inform policy. Using rescaled data from a UK general population survey seven separate random effects logit regressions were carried out for ordinal preferences and seven for cardinal differences. In contrast to previous studies the analyses found that ordinal preference models appear to give different results to cardinal data. Ordinal preference data performed worse than the cardinal difference suggesting that: (1) VAS contains at least some relevant and useful cardinal data and (2) ignoring such data worsens performance of the resulting measures.