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Abstract Details

Activity Number: 180
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #303992
Title: Comparison of Imputation Methods for Missing Values in AR(1) Longitudinal Studies
Author(s): Michikazu Nakai*+ and Yoshihiro Miyamoto and Kunihiro Nishimura
Companies: National Cerebral and Cardiovascular Center, Japan and National Cerebral and Cardiovascular Center, Japan and National Cerebral and Cardiovascular Center, Japan
Address: , , _, ,
Keywords: missing data ; imputation ; complete case ; LOCF ; longitudinal data ; AR1
Abstract:

Missing values often occur in longitudinal studies. It is known that Complete Case Method gives some biases and may cause incorrect analysis. In this presentation, we investigate the efficiency of several imputation methods in longitudinal studies with AR(1) covariance structure with four imputation methods including the Complete Case Method, Mean Imputation Method, Last Observation Carried Forward (LOCF) Method, and Multiple Imputation Method. Simulation studies are conducted with repeated measurements Yit (i=1 to 100;t=1 to 5) generated from a multivariate normal distribution with mean response E(Yit)=ß0+ß1t where ß0= intercept, ß1= slope and p|s-t| =correlation for p=0. The variance at each occasion is assumed to be constant over time, while the correlations have a first-order autoregressive (AR(1)) pattern with positive coefficient. Assuming that the first occasion is fully observed, simple random sampling without replacement is used to make MCAR (Missing Completely at Random) datasets and to test three different cases.Normality Shapiro-Wilk test is performed to each imputation method at each time point. At last, two different slope values (0.1 and 2) are tested.


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