This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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344
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #307047 |
Title:
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Longitudinal Data Analysis: Tackling Dropouts and Those Pesky Outliers
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Author(s):
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Devan V. Mehrotra*+ and Xiaoming Li and Jiajun Liu
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Companies:
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Merck Research Laboratories and Merck Research Laboratories and Merck Research Laboratories
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Address:
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351 N. Sumneytown Pike, North Wales, PA, 19446,
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Keywords:
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Missing Data ;
M-estimation ;
Multiple Imputation ;
Non-normality ;
Outliers ;
Robust regression
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Abstract:
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In a comparative clinical trial, endpoints of interest (e.g., bone density) are measured at baseline and fixed post-baseline time points. The resulting longitudinal data, often incomplete due to dropouts, are commonly analyzed using likelihood-based methods (e.g., REML) that assume multivariate normality of the response vector, conditional on covariates. If the normality assumption is untenable, semi-parametric methods like GEE or weighted GEE are sometimes recommended. To retain good performance under normality, but significantly increase efficiency under non-normality, we propose an easy-to-implement alternate approach that uses multiple imputation to tackle missing data and a subsequent robust parametric analysis to tackle potential non-normality and/or outliers. The efficiency gains using the proposed versus standard methods are illustrated using simulations and a real example.
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