Linked Life Data

9094918

Source:http://linkedlifedata.com/resource/pubmed/id/9094918

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4 Suppl
pubmed:dateCreated
1997-4-30
pubmed:abstractText
Regression calibration is a statistical method for adjusting point and interval estimates of effect obtained from regression models commonly used in epidemiology for bias due to measurement error in assessing nutrients or other variables. Previous work developed regression calibration for use in estimating odds ratios from logistic regression. We extend this here to estimating incidence rate ratios from Cox proportional hazards models and regression slopes from linear-regression models. Regression calibration is appropriate when a gold standard is available in a validation study and a linear measurement error with constant variance applies or when replicate measurements are available in a reliability study and linear random within-person error can be assumed. In this paper, the method is illustrated by correction of rate ratios describing the relations between the incidence of breast cancer and dietary intakes of vitamin A, alcohol, and total energy in the Nurses' Health Study. An example using linear regression is based on estimation of the relation between ultradistal radius bone density and dietary intakes of caffeine, calcium, and total energy in the Massachusetts Women's Health Study. Software implementing these methods uses SAS macros.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0002-9165
pubmed:author
pubmed:issnType
Print
pubmed:volume
65
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1179S-1186S
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Regression calibration method for correcting measurement-error bias in nutritional epidemiology.
pubmed:affiliation
Department of Epidemiology, School of Public Health, Boston, MA 02115, USA. stdis@gauss.bwh.harvard.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review