Correcting for measurement error in the analysis of case-control data with repeated measurements of exposure

Mimi Kim, Anne Zeleniuch-Jacquotte

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The authors present a technique for correcting for exposure measurement error in the analysis of case-control data when subjects have a variable number of repeated measurements, and the average is used as the subject's measure of exposure. The true exposure as well as the measurement error are assumed to be normally distributed. The method transforms each subject's observed average by a factor which is a function of the measurement error parameters, prior to fitting the logistic regression model. The resulting logistic regression coefficient estimate based on the transformed average is corrected for error. A bootstrap method for obtaining confidence intervals for the true regression coefficient, which takes into account the variability due to estimation of the measurement error parameters, is also described. The method is applied to data from a nested case-control study of hormones and breast cancer.

Original languageEnglish (US)
Pages (from-to)1003-1010
Number of pages8
JournalAmerican Journal of Epidemiology
Volume145
Issue number11
StatePublished - Jun 1 1997
Externally publishedYes

Fingerprint

Logistic Models
Case-Control Studies
Hormones
Confidence Intervals
Breast Neoplasms

Keywords

  • breast neoplasms
  • epidemiologic methods
  • hormones
  • measurement error
  • statistics

ASJC Scopus subject areas

  • Epidemiology

Cite this

Correcting for measurement error in the analysis of case-control data with repeated measurements of exposure. / Kim, Mimi; Zeleniuch-Jacquotte, Anne.

In: American Journal of Epidemiology, Vol. 145, No. 11, 01.06.1997, p. 1003-1010.

Research output: Contribution to journalArticle

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