Linked Life Data

20844438

Source:http://linkedlifedata.com/resource/pubmed/id/20844438

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-10-6
pubmed:abstractText
In cohort studies with common outcomes, the odds ratio estimated from a logistic regression analysis is often interpreted as an indirect estimate of the risk ratio. In such settings, the odds ratio will be farther from the null than the risk ratio. Direct and unbiased estimates of the risk ratio may be obtained from a log binomial model fit by maximum likelihood. When the maximum likelihood log binomial model fails to converge (as is common) or provides predicted probability estimates or upper confidence limits greater than 1.0, various approaches have been suggested, but each has drawbacks, as we describe. We propose a novel Bayesian approach for the estimation of the risk ratio from the log binomial model that addresses drawbacks of existing approaches. Posterior computation can be accomplished easily using the WinBUGs code provided.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1531-5487
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
855-62
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Estimation of risk ratios in cohort studies with common outcomes: a Bayesian approach.
pubmed:affiliation
Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. chux0051@umn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural