title: 1996 Remington lecture: modeling multivariate longitudinal data that are incomplete.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors: Section on Biostatistics, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA.
authors: M A
authors: MA
authors: Espeland
authors: T E
authors: TE
authors: Craven
authors: M E
authors: ME
authors: Miller
authors: R
authors: R
authors: D'Agostino
mesh_terms: D016893:Carotid Stenosis / Q000000981:diagnostic imaging / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015999:Multivariate Analysis; D016032:Randomized Controlled Trials as Topic; D014463:Ultrasonography