Linked Life Data

16158407

Source:http://linkedlifedata.com/resource/pubmed/id/16158407

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2006-1-3
pubmed:abstractText
The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
513-27
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A method for fitting regression splines with varying polynomial order in the linear mixed model.
pubmed:affiliation
Department of Biostatistics, The University of North Carolina, Chapel Hill, 27599, USA. Lloyd_Edwards@unc.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural