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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
4
|
| pubmed:dateCreated |
1998-1-5
|
| pubmed:abstractText |
Longitudinal data, or data that are repeated measurements on various subjects across time, are commonplace in biostatistical studies. The general linear mixed model is a useful statistical tool for analyzing such data and drawing meaningful inferences about them. This paper discusses some of the most common mixed models and fits them to a prototypical example involving repeated measures on blood pressure. Computer implementation is via the MIXED procedure in the SAS System, and code descriptions and output interpretations accompany the example.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Nov
|
| pubmed:issn |
1054-3406
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
7
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
481-500
|
| pubmed:dateRevised |
2007-11-15
|
| pubmed:meshHeading | |
| pubmed:year |
1997
|
| pubmed:articleTitle |
An example of using mixed models and PROC MIXED for longitudinal data.
|
| pubmed:affiliation |
SAS Institute Inc., Cary, North Carolina 27513, USA.
|
| pubmed:publicationType |
Journal Article,
Review
|