The limitations due to exposure detection limits for regression models

Enrique F. Schisterman, Albert Vexler, Brian W. Whitcomb, Aiyi Liu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)374-383
Number of pages10
JournalAmerican Journal of Epidemiology
Volume163
Issue number4
DOIs
StatePublished - Feb 2006
Externally publishedYes

Keywords

  • Bias (epidemiology)
  • Censored data
  • Epidemiology, molecular
  • Limit of detection
  • Regression analysis

ASJC Scopus subject areas

  • Epidemiology

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