title: The limitations due to exposure detection limits for regression models.
abstract: Biomarker use in exposure assessment is increasingly common, and consideration of related issues is of growing importance. Exposure quantification may be compromised when measurement is subject to a lower threshold. Statistical modeling of such data requires a decision regarding the handling of such readings. Various authors have considered this problem. In the context of linear regression analysis, Richardson and Ciampi (Am J Epidemiol 2003;157:355-63) proposed replacement of data below a threshold by a constant equal to the expectation for such data to yield unbiased estimates. Use of such an imputation has some limitations; distributional assumptions are required, and bias reduction in estimation of regression parameters is asymptotic, thereby presenting concerns about small studies. In this paper, the authors propose distribution-free methods for managing values below detection limits and evaluate the biases that may result when exposure measurement is constrained by a lower threshold. The authors utilize an analytical approach and a simulation study to assess the effects of the proposed replacement method on estimates. These results may inform decisions regarding analytical plans for future studies and provide a possible explanation for some amount of the discordance seen in extant literature.
authors.affiliation: Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Boulevard, Rm. 7B03, Rockville, MD 20852, USA. schistee@mail.nih.gov
authors.forename: Enrique F
authors.initials: EF
authors.lastname: Schisterman
authors.forename: Albert
authors.initials: A
authors.lastname: Vexler
authors.forename: Brian W
authors.initials: BW
authors.lastname: Whitcomb
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
mesh_terms: D015982:Bias; D015415:Biomarkers; D003657:Decision Making; D004781:Environmental Exposure / Q000032:analysis / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D009010:Monte Carlo Method; D018570:Risk Assessment; D016008:Statistical Distributions
chemical_list: D015415:Biomarkers