Abstract

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained, and thus are left censored. Common practice for addressing the censoring issue is to delete or 'fill-in' the censored observations in data analysis, which often results in biased or non-efficient estimates. Assuming the concentration or transformed concentration follows a normal distribution, a Tobit regression model can be applied. When the study population is heterogeneous, for example due to the existence of a latent group of subjects who lack the substance, the problem becomes more challenging. In this paper, we conduct intensive simulation studies to investigate the statistical issues in analyzing censored data and compare different methods in which the data are treated either as a dependent variable or an independent variable. We also analyze triclosan data in the NHANES study and metabolites data in the Bogalusa Heart Study to illustrate the issues. Some guidelines for analyzing such censored data are provided.

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