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Analysis of incomplete multivariate data using linear models with structured covariance matrices
Author(s) -
Schluchter Mark D.
Publication year - 1988
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.4780070132
Subject(s) - univariate , missing data , multivariate statistics , covariance , covariate , statistics , multivariate analysis , computer science , linear model , generality , range (aeronautics) , random effects model , mathematics , econometrics , medicine , psychology , materials science , meta analysis , composite material , psychotherapist
Incomplete and unbalanced multivariate data often arise in longitudinal studies due to missing or unequally‐timed repeated measurements and/or the presence of time‐varying covariates. A general approach to analysing such data is through maximum likelihood analysis using a linear model for the expected responses, and structural models for the within‐subject covariances. Two important advantages of this approach are: (1) the generality of the model allows the analyst to consider a wider range of models than were previously possible using classical methods developed for balanced and complete data, and (2) maximum likelihood estimates obtained from incomplete data are often preferable to other estimates such as those obtained from complete cases from the standpoint of bias and efficiency. A variety of applications of the model are discussed, including univariate and multivariate analysis of incomplete repeated measures data, analysis of growth curves with missing data using random effects and time‐series models, and applications to unbalanced longitudinal data.