JSM 2004 - Toronto

Abstract #300580

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Activity Number: 303
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #300580
Title: Comparison of Two SAS Procedures for Longitudinal Data with Evaluation of SAS Experimental Procedure Multiple Imputations
Author(s): Zoran Bursac*+ and Barbara Neas
Companies: University of Arkansas for Medical Sciences and University of Oklahoma Health Sciences Center
Address: 4301 W. Markham St., Slot 820, Little Rock, AR, 72205,
Keywords: incomplete data analysis ; imputation methods ; longitudinal data
Abstract:

We compared the performance of SAS procedures MIXED and GENMOD in fitting multiple-population models to longitudinal growth data with missing values. Comparison was done under 40 simulated conditions that included two different percentages of missing data, high and medium mixed correlation structures, four and seven repeated time points, and five levels of data completeness. Completeness levels included complete data, data missing at random (MAR), MAR with imputed values, nonignorable missing data mechanism (NI) and NI with imputed values. Data imputation was performed using SAS experimental procedure MI. We found that PROC MIXED performed with as good or higher accuracy than PROC GENMOD under more than 90% of experimental conditions and that multiple data imputations helped improve the model components only for NI missing data mechanism.

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