The question of how to analyze unbalanced or incomplete repeated-measures data is a common problem facing analysts. We address this problem through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within-subject covariances. Models that can be fit include standard univariate and multivariate models with incomplete data, random-effects models, and models with time-series and factor-analytic error structures. We describe Newton-Raphson and Fisher scoring algorithms for computing maximum likelihood estimates, and generalized EM algorithms for computing restricted and unrestricted maximum likelihood estimates. An example fitting several models to a set of growth data is included.

Educational disadvantage associated with very low birth weight persists into early adulthood.

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The predictive validity of a subnormal MDI for cognitive function at school age is poor but better for ELBW children who have neurosensory impairments. We are concerned that decisions to provide intensive care for ELBW infants in the delivery room might be biased by reported high rates of cognitive impairments based on the use and presumptive validity of the BSID II MDI.

VLBW females catch up in growth by 20 years of age whereas VLBW males remain significantly shorter and lighter than controls. Since catch-up growth may be associated with metabolic and cardiovascular risk later in life, these findings may have implications for the future adult health of VLBW survivors.