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1996 Remington lecture: modeling multivariate longitudinal data that are incomplete
M A Espeland1, T E Craven, M E Miller
1Section on Biostatistics, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA.
Purpose:
We describe the impact that missing data may have on model selection for longitudinal multivariate data.
Methods:
Maximum likelihood was used to fit several models to ultrasonographic measurements from the Asymptomatic Carotid Artery Progression Study (ACAPS). Graphical techniques were used to examine evidence concerning the underlying missing data mechanisms associated with each model.
Results:
Using statistical methodology that addressed missing data substantially increased the statistical efficiency of our analysis of ultrasonographic data. Only complex models that included segment-specific parameterizations for longitudinal correlations appeared to allow missing data to be assumed to occur at random.
Conclusion:
Ignoring the nature of missing data in conducting statistical analyses can have serious consequences when missingness is not rare. It may be necessary to fit models of high dimension with maximum likelihood techniques to address missing data appropriately, however these approaches may improve statistical efficiency.