A bias-adjusted estimator in quantile regression for clustered data

TL;DR

This paper introduces a bias-adjusted two-step estimator for quantile regression with clustered data, addressing bias issues in fixed effects estimation, and provides a bootstrap method for inference, validated through simulations and an AIDS study.

Contribution

It proposes a novel two-step estimation method and bootstrap procedure to reduce bias in quantile regression for small-cluster data, improving inference accuracy.

Findings

Existing methods can produce severely biased fixed effects estimates.

The proposed approach reduces bias and improves confidence interval coverage.

Simulation and real data demonstrate the method's effectiveness.

Abstract

The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed {by a pseudo likelihood approach (the LQMM method)} and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.