Quantile regressions as a tool to evaluate how an exposure shifts and reshapes the outcome distribution: a primer for epidemiologists

Abstract

Quantifying how an exposure affects the entire outcome distribution is often important (eg, for outcomes such as blood pressure that have nonlinear effects on long-term morbidity and mortality). Quantile regressions offer a powerful way of estimating an exposure's relationship with the outcome distribution, but they remain underused in epidemiology. We introduce quantile regressions with a focus on distinguishing estimators for quantiles of the conditional and unconditional outcome distributions. We also present an empirical example in which we fit mean and quantile regressions to investigate educational attainment's association with later-life systolic blood pressure (SBP). We use data on 8875 US-born respondents aged 50 years or older from the US Health and Retirement Study. Having more education was negatively associated with mean SBP. Conditional and unconditional quantile regressions both suggested a negative association between education and SBP at all levels of SBP, but the absolute magnitudes of these associations were higher at higher SBP quantiles relative to lower quantiles. In addition to showing that educational attainment shifted the SBP distribution leftward, quantile regression results revealed that education may have reshaped the SBP distribution through larger protective associations in the right tail, thus benefiting those at highest risk of cardiovascular diseases.