Eleanor M Pullenayegum1, Hoi Suen Wong2, Aaron Childs3. 1. St Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada (EMP) 2. University of Toronto, Toronto, Ontario, Canada (HSW) 3. McMaster University, Hamilton, Ontario, Canada (AC)
Abstract
BACKGROUND: Measured utility data have a discrete distribution, and the discreteness is particularly pronounced for EQ-5D utilities. Given the discreteness of the data, modeling the distribution parametrically is likely to be difficult. Moreover, since the distribution is bounded, the linearity assumptions made by many models are questionable. This article suggests using semi-parametric models and illustrates the use of generalized additive models (GAMs) for handling nonlinear associations. METHODS: A simulation study is used to explore whether bias arises when applying parametric models to discrete utility data. A further simulation investigates the bias in semi-parametric linear and quasi-beta regression models when the assumed linearity does not hold and also investigates the use of GAMs. The use of GAMs in practice is shown through a recent study of health utilities among patients with diabetes. RESULTS: Using parametric beta models to analyze discrete EQ-5D utility data led to substantial bias. Both semi-parametric linear regression and quasi-beta regression led to biased estimates of marginal and incremental effects when the mean model was misspecified. The use of GAMs reduced these biases. CONCLUSIONS: Parametric models for EQ-5D utility data should be used with caution. Semi-parametric modeling of utility data should check for nonlinearity. GAMs can help in diagnosing and accommodating nonlinearity.
BACKGROUND: Measured utility data have a discrete distribution, and the discreteness is particularly pronounced for EQ-5D utilities. Given the discreteness of the data, modeling the distribution parametrically is likely to be difficult. Moreover, since the distribution is bounded, the linearity assumptions made by many models are questionable. This article suggests using semi-parametric models and illustrates the use of generalized additive models (GAMs) for handling nonlinear associations. METHODS: A simulation study is used to explore whether bias arises when applying parametric models to discrete utility data. A further simulation investigates the bias in semi-parametric linear and quasi-beta regression models when the assumed linearity does not hold and also investigates the use of GAMs. The use of GAMs in practice is shown through a recent study of health utilities among patients with diabetes. RESULTS: Using parametric beta models to analyze discrete EQ-5D utility data led to substantial bias. Both semi-parametric linear regression and quasi-beta regression led to biased estimates of marginal and incremental effects when the mean model was misspecified. The use of GAMs reduced these biases. CONCLUSIONS: Parametric models for EQ-5D utility data should be used with caution. Semi-parametric modeling of utility data should check for nonlinearity. GAMs can help in diagnosing and accommodating nonlinearity.