Premium
Regression models for the analysis of longitudinal Gaussian data from multiple sources
Author(s) -
O'Brien Liam M.,
Fitzmaurice Garrett M.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2056
Subject(s) - covariance , computer science , covariance matrix , statistics , longitudinal data , regression analysis , regression , gaussian , mathematics , econometrics , data mining , physics , quantum mechanics
We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. Copyright © 2005 John Wiley & Sons, Ltd.