I-Chen Chen1, Stephen J Bertke2, Brian D Curwin2. 1. Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, USA. okv0@cdc.gov. 2. Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, USA.
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.
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.
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