Algorithms for mapping descriptive measures of health status into preference-based measures are now widely available and their application in economic evaluation is increasingly commonplace. Existing algorithms make use of scale, subscale, or item scores on descriptive measures. Item-based algorithms entail fewer restrictions than their scale or subscale-based equivalents but are subject to problems in estimation and application. The objective of the present study is to quantify any loss of predictive validity associated with using subscale or scale scores (rather than item scores) to derive conversion algorithms.
Multiple linear regression methods to derive item-based, subscale-based, and scale-based algorithms for mapping SF-36 data into Assessment of Quality of Life (AQoL) utility scores in a stratified sample of persons aged more than 16 years and resident in Victoria, Australia. The theoretical consistency and predictive validity of competingalgorithms is evaluated against criteria reflecting the intended use of predicted utility scores.
Three mappings were suitable for between-group comparisons. There was no discernible increase in error associated with a move from the item-based mapping to either the subscale- or scale-based mapping.
Our results do not support the hypothesis that fewer restrictions on functional form necessarily result in a lower magnitude of error when predicting between-group differences. Rather, it appears that the subscale-based mapping offers a good compromise—requiring fewer restrictions on the form of the relationship between SF-36 responses and the AQoL utility score than the scale-based mapping and permitting a more efficient use of SF-36 data than the item-based mapping.