Abstract Details
Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #308523 |
Title:
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Simulation Study to Compare New Hybrid Model with Pattern Mixture Model Under Missing Not at Random
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Author(s):
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Fang Liu*+ and Jingjing Chen
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Companies:
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Merck & Co., Inc and Accenture PLC
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Keywords:
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Missing data ;
MNAR ;
dropouts
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Abstract:
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The analysis of longitudinal data in clinical trial presents a challenge as there are often missing data points. When data missing is caused by study design, the missingness cannot be assumed as at random (MAR) but not at random (MNAR). A hybrid model was proposed to impute missing data by incorporating multiple imputation for MAR and single imputation for MNAR classified by dropout reasons. For example, Dropouts due to adverse events and lack of efficacy are classified as MAR, while dropouts due to loss to follow-up and others are classified as MNAR. Simulations will be carried out to compare the hybrid model with pattern mixture model under Missing Not at Random (MNAR).
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Authors who are presenting talks have a * after their name.
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