Abstract Details
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Activity Number:
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457
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #311974
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View Presentation
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Title:
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A Simulation Study to Compare Inverse Probability Weighting with Other Commonly Used Missing Data Imputation Methods for Binary Outcome Variables
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Author(s):
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Fang Liu*+ and Chen Jingjing
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Companies:
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Merck and MedImmune
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Keywords:
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missing at random ;
multiple imputation ;
inverse probability weighting ;
IPW GEE ;
repeated measure
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Abstract:
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The analysis of longitudinal data in clinical trials presents a challenge as there are often missing data points. When binary outcomes variables are involved, the missing data imputation methods may become complicated. A simulation study illustrates how Generalized Linear Mixed Model (GLMM), Inverse Probability Weighted (IPW) Generalized Estimation Equation (GEE) method, multiple imputation and doubly robust method work in practice, especially for binary outcome variables in terms of efficiency and accuracy, with MAR assumption.
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Authors who are presenting talks have a * after their name.
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