Publication: Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error
No Thumbnail Available
Open/View Files
Date
1992
Authors
Published Version
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Rosner, B., D. Spiegelman, and W. C. Willett. 1992. “Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error.” American Journal of Epidemiology 136 (11): 1400–1413. https://doi.org/10.1093/oxfordjournals.aje.a116453.
Research Data
Abstract
Frequently, covariates used in a logistic regression are measured with error. The authors Previously described the correction of logistic regression relative risk estimates for measurement error in one or more covariates when a ''gold standard'' is available for exposure assessment. For some exposures (e.g., serum cholesterol), no gold standard exists, and one must assess measurement error via a reproducibility substudy. In this paper, the authors present measurement error methods for logistic regression when there is error (possibly correlated) in one or more covariates and one has data from both a main study and a reproducibility substudy. Confidence intervals from this procedure reflect error in parameter estimates from both studies. These methods are applied to the Framingham Heart Study. where the 10-year incidence of coronary heart disease is related to several coronary risk factors among 1,731 men disease-free at examination 4. Reproducibility data are obtained from the subgroup of 1,346 men seen at examinations 2 and 3. Estimated odds ratios comparing extreme quintiles for risk factors with substantial error were increased after correction for measurement error (serum cholesterol, 2.2 vs. 2.9; serum glucose, 1.3 vs. 1.5; systolic blood pressure, 2.8 vs. 3.8). but were generally decreased or unchanged for risk factors with little or no error (body mass index, 1 .6 vs 1.6. age 65-69 years vs. 35-44 years, 4.3 vs. 3.8; smoking, 1.7 vs. 1.7).
Description
Other Available Sources
Keywords
Terms of Use
Metadata Only