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Activity Number:
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209
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #309631 |
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Title:
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Application of the Pattern-Mixture Latent Trajectory Model in an Epidemiological Study
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Author(s):
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Hiroko Dodge*+ and Changyu Shen and Mary Ganguli
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Companies:
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Oregon State University and Indiana University and University of Pittsburgh
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Address:
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3066 Willakenzie Road, Eugene, OR, 97401,
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Keywords:
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Latent Trajectory Model ; non-ignorable missing data bias ; PROC TRAJ
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Abstract:
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Longitudinal designs, requiring follow-up of the same individuals over time, are increasingly common in epidemiological studies. However, missing data bias is a major problem in longitudinal studies where attrition is inevitable over time. Restricting analyses to only the observed data could bias the results depending on the types of missingness. One approach to address non-ignorable missing data bias is a pattern mixture model, but un-identifiability is a problem. We offer a practical solution to this problem by using latent trajectory analysis implemented in the SAS TRAJ procedure, which identifies latent groups with different trajectory patterns. The approach presented here is appealing since it can be easily implemented using common software and can be applied to wide variety of disciplines which analyze longitudinal data with potentially non-ignorable missing data.
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