Skip to main content
Open access
Research Article
1 December 1998

Measurement error, biases, and the validation of complex models for blood lead levels in children.

Publication: Environmental Health Perspectives
Volume 106, Issue suppl 6
Pages 1535 - 1539

Abstract

Measurement error causes biases in regression fits. If one could accurately measure exposure to environmental lead media, the line obtained would differ in important ways from the line obtained when one measures exposure with error. The effects of measurement error vary from study to study. It is dangerous to take measurement error corrections derived from one study and apply them to data from entirely different studies or populations. Measurement error can falsely invalidate a correct (complex mechanistic) model. If one builds a model such as the integrated exposure uptake biokinetic model carefully, using essentially error-free lead exposure data, and applies this model in a different data set with error-prone exposures, the complex mechanistic model will almost certainly do a poor job of prediction, especially of extremes. Although mean blood lead levels from such a process may be accurately predicted, in most cases one would expect serious underestimates or overestimates of the proportion of the population whose blood lead level exceeds certain standards.

Formats available

You can view the full content in the following formats:

Information & Authors

Information

Published In

Environmental Health Perspectives
Volume 106Issue suppl 6December 1998
Pages: 1535 - 1539
PubMed: 9860912

History

Published online: 1 December 1998

Authors

Affiliations

R J Carroll
Department of Statistics, Texas A&M University, College Station 77843-3143, USA. [email protected]
C D Galindo
Department of Statistics, Texas A&M University, College Station 77843-3143, USA. [email protected]

Metrics & Citations

Metrics

About Article Metrics


Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click DOWNLOAD.

Cited by

  • Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates, Mathematics, 10.3390/math12020309, 12, 2, (309), (2024).
  • DNA methylation arrays as surrogate measures of cell mixture distribution, BMC Bioinformatics, 10.1186/1471-2105-13-86, 13, 1, (2012).
  • Measurement error in environmental epidemiology and the shape of exposure-response curves, Critical Reviews in Toxicology, 10.3109/10408444.2011.563420, 41, 8, (651-671), (2011).
  • Expanding the Scope of Risk Assessment: Methods of Studying Differential Vulnerability and Susceptibility, American Journal of Public Health, 10.2105/AJPH.2011.300367, 101, S1, (S102-S109), (2011).
  • Evaluating measurement error in estimates of worker exposure assessed in parallel by personal and biological monitoring, American Journal of Industrial Medicine, 10.1002/ajim.20422, 50, 2, (112-121), (2007).
  • Development of a System to Predict Feed Protein Flow to the Small Intestine of Cattle, Journal of Dairy Science, 10.3168/jds.S0022-0302(05)72686-2, 88, 1, (282-295), (2005).
  • Accuracy and Precision of Computer Models to Predict Passage of Crude Protein and Amino Acids to the Duodenum of Lactating Cows, Journal of Dairy Science, 10.3168/jds.S0022-0302(01)74520-1, 84, 3, (649-664), (2001).

View Options

View options

PDF

View PDF

Get Access

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share on social media