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

Abstract

AbstractMost regression models estimate an exposure’s association with the mean value of the outcome, but quantifying how an exposure affects the entire outcome distribution is often important (e.g., when the outcome has non-linear relationships with risk of other adverse outcomes). Quantile regressions offer a powerful way of estimating an exposure’s relationship with the outcome distribution but remain underused in epidemiology. We introduce quantile regressions and then present an empirical example in which we fit mean and quantile regressions to investigate the association of educational attainment with later-life systolic blood pressure (SBP). We use data on 8,875 US-born respondents aged 50+ years from the Health and Retirement Study. 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. While all estimators showed more education was associated with a leftward shift of the SBP distribution, quantile regression results additionally 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.