Linked Life Data

16206247

Source:http://linkedlifedata.com/resource/pubmed/id/16206247

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
21
pubmed:dateCreated
2005-10-13
pubmed:abstractText
The semi-parametric regression achieved via penalized spline smoothing can be expressed in a linear mixed models framework. This allows such models to be fitted using standard mixed models software routines with which many biostatisticians are familiar. Moreover, the analysis of complex correlated data structures that are a hallmark of biostatistics, and which are typically analysed using mixed models, can now incorporate directly smoothing of the relationship between an outcome and covariates. In this paper we provide an introduction to both linear mixed models and penalized spline smoothing, and describe the connection between the two. This is illustrated with three examples, the first using birth data from the U.K., the second relating mammographic density to age in a study of female twin-pairs and the third modelling the relationship between age and bronchial hyperresponsiveness in families. The models are fitted in R (a clone of S-plus) and using Markov chain Monte Carlo (MCMC) implemented in the package WinBUGS.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3361-81
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Tutorial in biostatistics: spline smoothing with linear mixed models.
pubmed:affiliation
Epidemiology and Biostatistics Unit, University of Melbourne, Australia. lgurrin@unimelb.edu.au
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't