Applications for Quantile Regression in Epidemiology

Abstract

To illustrate the utility of quantile regression in epidemiology for outcomes that are continuous and when exposure effects may differ across the distribution of the outcome. Linear regression methods estimate only the effects at the mean level which may be an incomplete and biased summary of the effect of exposures for some continuous health outcomes. There are several variations of the quantile regression method including classical linear quantile regression, nonparametric quantile regression for growth trajectories, and the modified quantile regression for case–control designs. Such methods offer several applications including (1) the use of quantile regression to test whether the effects of exposure are similar across quantiles, (2) the use of quantile regression for risk prediction, and (3) the use of quantile regression to examine the effects of growth trajectories over time. Quantile regression is an important tool for understanding continuous health outcomes, especially outcomes that are not normally distributed, as it offers insight into the relation of exposures with respect to the distribution of the outcome. Quantile regression methods have the potential to deepen and expand the existing quantitative evidence from more common mean-based analyses.