Dataset Information

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 does not require any specified error distributions, with the substitution method for skewed data with repeated measurements and non-detects.

Results

In a simulation study and application example, we demonstrate that this method performs well, particularly for highly right-skewed data, as parameter estimates are consistent and have smaller mean squared error relative to existing approaches.

Significance

The proposed approach provides an alternative insight into the conditional distribution of an exposure outcome for repeated measures models.

SUBMITTER: Chen IC

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

REPOSITORIES: biostudies-literature

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Publications

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

Chen I-Chen IC Bertke Stephen J SJ Curwin Brian D BD

Journal of exposure science & environmental epidemiology 20210609 6

<h4>Background</h4>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 b ...[more]

PMID: 34108633

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Project description:Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.

Dataset Information

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

Background

Objective

Methods

Results

Significance

Publications

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

Similar Datasets

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