Linked Life Data

20191599

Source:http://linkedlifedata.com/resource/pubmed/id/20191599

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2010-5-25
pubmed:abstractText
Meta-analysis of randomized controlled trials based on aggregated data is vulnerable to ecological bias if trial results are pooled over covariates that influence the outcome variable, even when the covariate does not modify the treatment effect, or is not associated with the treatment. This paper shows how, when trial results are aggregated over different levels of covariates, the within-study covariate distribution, and the effects of both covariates and treatments can be simultaneously estimated, and ecological bias reduced. Bayesian Markov chain Monte Carlo methods are used. The method is applied to a mixed treatment comparison evidence synthesis of six alternative approaches to post-stroke inpatient care. Results are compared with a model using only the stratified covariate data available, where each stratum is treated as a separate trial, and a model using fully aggregated data, where no covariate data are used.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2010 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
30
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1340-56
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Controlling ecological bias in evidence synthesis of trials reporting on collapsed and overlapping covariate categories.
pubmed:affiliation
Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK. l.govan@clinmed.gla.ac.uk
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't