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Quantile Regression for Exposure Data with Repeated Measures in the Presence of Non-Detects
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11 2021
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Source: J Expo Sci Environ Epidemiol. 31(6):1057-1066
[PDF-477.16 KB]
Details:
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Alternative Title:J Expo Sci Environ Epidemiol
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Personal Author:
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Description: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 modelling the geometric mean operate under the assumption the data is 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. Additionally, 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.
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Pubmed ID:34108633
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Pubmed Central ID:PMC8595850
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