Linked Life Data

11746328

Source:http://linkedlifedata.com/resource/pubmed/id/11746328

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2001-12-17
pubmed:abstractText
Measurement error in exposures and confounders leads to bias in regression coefficients. It is possible to adjust for this bias if true values or independent replicates are observed on a subsample. We extend a method suitable for quantitative variables to the situation where both binary and quantitative variables are present. Binary variables with independent replicates introduce two extra problems: (i) the error is correlated with the true value, and (ii) the measurement error probabilities are unidentified if only two replicates are available. We show that - under plausible assumptions - adjustment for error in binary confounders does not need to address these problems. The regression coefficient for a binary exposure is overadjusted if methods for continuous variables are used. Correct adjustment is possible either if three replicates are available, or if further assumptions can be made; otherwise, bounds can be put on the correctly adjusted value, and these bounds are reasonably close together if the exposure has prevalence near 0.5.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2001 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3441-57
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Correcting for measurement error in binary and continuous variables using replicates.
pubmed:affiliation
Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. ian.white@mrc-bsu.cam.ac.uk
pubmed:publicationType
Journal Article