Inference for censored quantile regression models in longitudinal studies

TL;DR

This paper develops new inference procedures for censored longitudinal data using semi-parametric quantile regression, addressing challenges in biomedical studies with censored measurements and intra-subject dependencies.

Contribution

It introduces a rank score test for large sample inference in censored longitudinal quantile regression models, accounting for censoring and intra-subject correlation.

Findings

Proposed methods perform well in simulations.

Application to AIDS data demonstrates practical utility.

Framework effectively differentiates predictor influences.

Abstract

We develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants. We consider a semi-parametric quantile regression model that makes no distributional assumptions. Our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit. In such studies the focus is often on testing hypotheses about treatment equality. To this end, we propose a rank score test for large sample inference on a subset of the covariates. We demonstrate the importance of accounting for both censoring and intra-subject dependency and evaluate the performance of our proposed methodology in a simulation study. We then apply the proposed inference procedures to data from an AIDS-related clinical trial. We conclude that our framework and proposed methodology is very valuable for differentiating the influences of predictors at different locations in the conditional distribution of a response variable.