Performance of quantile regression methods with discrete outcomes: A simulation study with applications to environmental epidemiology.

ABSTRACT:

Background

Quantile regression helps identify how associations vary across the outcome variable's distribution. Using simulations and data from the Maternal-Infant Research on Environmental Chemicals study, we showed that frequentist quantile regression can produce implausible results where the point estimates are integers or rational numbers and the outcome variable is discrete, which is common in health research. Applying "dithering" (also known as jittering) or using Bayesian quantile regression can prevent such implausible results, but the optimal strategy is unclear.

Methods

We conducted simulations with discrete outcomes to compare the bias and variability of point estimates of undithered frequentist, dithered frequentist, and Bayesian quantile regression. We also comp

SUBMITTER: Alampi JD 

PROVIDER: S-EPMC12571108 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature