Abstract #300632

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JSM 2003 Abstract #300632
Activity Number: 272
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract - #300632
Title: Imputation Using a Factor-Analysis Model for High-Dimensional Incomplete Longitudinal Data
Author(s): Jianming Wang*+ and Thomas R. Belin
Companies: Quintiles and University of California, Los Angeles
Address: 325 E Hillcrest Dr., Thousand Oaks, CA, 91360,
Keywords: factor-analysis ; longitudinal ; incomplete ; high-dimensional
Abstract:

Longitudinal datasets often suffer from missing values. Because of the large number of variables in these datasets, even a small rate of missingness on some variables can result in a large number of incomplete cases. 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 overparameterized. 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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