Utility-based quality-of-life measures are generally recommended for economic evaluation. However, disease-specific, non-utility outcome measures are used more frequently than the former, particularly in sleep health studies. This paper aimed to establish a mapping algorithm between two of the most used instruments in sleep health and the general population, respectively: the Functional Outcomes of Sleep Questionnaire 10 items (FOSQ-10) and the EuroQoL 5 Dimensions 5 Level (EQ-5D-5 L). In-sample cross-validation approach was performed using a k-fold technique to randomly divide the primary dataset (n = 1,514) into ten subsamples. Five regression techniques were employed to estimate utilities from FOSQ-10, including ordinary least squares, censored least absolute deviations, generalised linear model (GLM), GLM inverse and beta-binomial models. Six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation distribution of predicted utilities, residual distributions and proportion of predictions with absolute errors of less than 10% and 25% were used to evaluate the predictive ability of 55 regression models. Beta Binomial model 9 (BB (9)) was chosen as the best-fitting model based on the selection criteria rankings. The BB (9) employed a multivariable fractional polynomial technique, where all significant FOSQ-10 domains, obstructive sleep apnoea (OSA) diagnosis status, income and education level were included as continuous variables. RMSE (0.1997) and MAE (0.1433) estimates for this model were within the range of published estimates. When data on utility-based measures have not been collected directly, the mapping algorithm can be utilised to predict utility where only FOSQ-10 data are available.
Natsky et al. (Sat,) studied this question.