OBJECTIVES: The NICE methods guide update explicitly recommends adjustment of utilities for age using the multiplicative method. However, little discussion is provided in the new guidance or in the literature on potential limitations of the multiplicative method in terms of scaling, bounding and generalisability across different preference-based utility measures (PBUMs). Furthermore, the multiplicative method is unsuitable for handling negative health state utility values (HSUVs) or preventing unobtainable HSUVs for PBUMs where the lowest attainable utility value excluding death (u^lower_alive) is above 0. We aimed to explore the use of linear transformation to address limitations with the standard multiplicative method.

METHODS: We propose using a multiplicative with linear transformation (MLT) approach based on u^lower_alive of the chosen PBUM such that, when adjusting HSUVs for age and sex, the original HSUV and the age and sex-matched general population utility are transformed to a 0-1 scale before generating a HSUV multiplier. The general population utility for the chosen age and sex characteristics is then also transformed to a 0-1 scale for applying the utility multiplier, before transforming back to the original PBUM scale to produce an age and sex adjusted HSUV.

RESULTS: As several terms required for the linear transformations cancel out throughout the utility adjustment process, this results in a simple extension to the multiplicative method that only requires the incorporation of several u^lower_alive terms into the formula. The MLT approach produces different HSUV extrapolations by age for PBUMs when u^lower_alive is not 0, and in many cases may produce lower total QALYs in economic analyses versus the multiplicative method.

CONCLUSIONS: The MLT method may help address potential issues with the multiplicative method and provide better generalisability across different PBUMs when adjusting HSUVs for age and sex, without much additional complexity. This method is also extendable to adjusting disutilities and combining utilities for multiple comorbidities.