Back to Search
Start Over
The Limitations due to Exposure Detection Limits for Regression Models.
- Source :
- American Journal of Epidemiology; Feb2006, Vol. 163 Issue 4, p374-383, 10p, 9 Charts, 1 Graph
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00029262
- Volume :
- 163
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- American Journal of Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 19626432
- Full Text :
- https://doi.org/10.1093/aje/kwj039