Quantile regression for exposure data with repeated measures in the presence of non-detects.

ABSTRACT:

Background

Exposure data with repeated measures from occupational studies are frequently right-skewed and left-censored. To address right-skewed data, data are generally log-transformed and analyses modeling the geometric mean operate under the assumption the data are log-normally distributed. However, modeling the mean of exposure may lead to bias and loss of efficiency if the transformed data do not follow a known distribution. In addition, left censoring occurs when measurements are below the limit of detection (LOD).

Objective

To present a complete illustration of the entire conditional distribution of an exposure outcome by examining different quantiles, rather than modeling the mean.

Methods

We propose an approach combining the quantile regression model, which d

SUBMITTER: Chen IC 

PROVIDER: S-EPMC8595850 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature