Linear discriminant models for unbalanced longitudinal data.
This paper discusses statistical methods for the classification of observations into one of two or more groups based on longitudinal observations. Measurements on subjects in longitudinal medical studies are often collected at different times and on a different number of occasions. Classical multivariate methods for linear discriminant analysis are difficult to apply to repeated measurements due to the highly unbalanced structure observed in these data. Linear models for the analysis of longitudinal data proposed by Laird and Ware and non-linear models proposed by Lindstrom and Bates can be used to estimate population parameters for a discriminant model that classifies individuals into distinct predefined groups or populations. An example is presented using data from a study in 150 pregnant women in Santiago, Chile, in order to predict normal versus abnormal pregnancy outcomes.
Marshall
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Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Santiago, Chile. gm@mat.puc.cl
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D000022:Abortion, Spontaneous; D000704:Analysis of Variance; D016002:Discriminant Analysis; D005260:Female; D005865:Gestational Age; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D011247:Pregnancy; D011248:Pregnancy Complications; D011256:Pregnancy Outcome; D011336:Probability; D012372:ROC Curve; D013997:Time Factors