Regression models for the analysis of longitudinal Gaussian data from multiple sources.
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.
O'Brien
Liam M
LM
Department of Mathematics, Colby College, 5838 Mayflower Hill, Waterville, ME 04901, USA. lobrien@colby.edu
Fitzmaurice
Garrett M
GM
D000293:Adolescent; D000328:Adult; D002648:Child; D003627:Data Interpretation, Statistical; D005190:Family / Q000523:psychology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D019964:Mood Disorders / Q000523:psychology; D015999:Multivariate Analysis; D016011:Normal Distribution; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis
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