Abstract #300804


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JSM 2002 Abstract #300804
Activity Number: 183
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing*
Abstract - #300804
Title: Handling Incomplete High Dimensional Longitudinal Data by Multiple Imputation Using a Longitudinal Factor Analysis Model
Author(s): Jianming Wang*+ and Thomas Belin
Affiliation(s): University of California, Los Angeles and University of California, Los Angeles
Address: UCLA School of Public Health, Los Angeles, California, 90095, USA
Keywords: Incomplete high dimensional longitudinal data ; Multiple imputation ; Longitudinal factor analysis model
Abstract:

Multiple imputation is often used to handle missing data problems. When producing multiple imputations for the missing values, it is recommended that as many variables as possible be included. However, when the sample size is not large, a model with a large number of variables may easily be over-parameterized. Song and Belin (1999) introduced a method to overcome this difficulty by using a factor analysis model, which can reduce the number of parameters substantially. For longitudinal data, the factor model has the limitation of not reflecting the longitudinal structure. To overcome this deficiency, we develop a longitudinal factor analysis model, combining a factor structure to reflect cross-sectional correlations with a multivariate linear-mixed-model structure to reflect longitudinal correlations. The method is illustrated using data from a study comparing two oral surgery treatments, where several clinical and psychological outcomes were measured longitudinally.


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