Linked Life Data

17384079

Source:http://linkedlifedata.com/resource/pubmed/id/17384079

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2007-9-26
pubmed:abstractText
The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences among subjects at the start of the study (baseline characteristics, cross-sectional effects). Often, especially in observational studies, longitudinal trends are studied after correction for many potentially important baseline differences between subjects. We show that, in the context of linear mixed models, inference for longitudinal trends is in general biased if a wrong model for the baseline characteristics is used. However, we will argue that this bias is small in most practical situations and completely vanishes in the special case of a growth curve model for complete balanced data. In the latter case, inference for longitudinal trends is completely independent of additional baseline covariates that might have been omitted from the model.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1465-4644
pubmed:author
pubmed:issnType
Print
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
772-83
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
The effect of miss-specified baseline characteristics on inference for longitudinal trends in linear mixed models.
pubmed:affiliation
Biostatistical Centre, Katholieke Universiteit Leuven, U.Z. St.-Rafaël. Kapucijnenvoer 35, B-3000 Leuven, Belgium. geert.verbeke@med.kuleuven.be
pubmed:publicationType
Journal Article