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Activity Number:
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26
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304629 |
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Title:
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Pseudo Maximum Likelihood Approach for Multivariate Longitudinal Data Analysis Subject to Left-Censoring
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Author(s):
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Ghideon Ghebregiorgis*+ and Lisa A. Weissfeld
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Companies:
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FDA and University of Pittsburgh
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Address:
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1401 Rockville Pike, 400S/HFM-217, Rockville, MD, 20852,
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Keywords:
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Pseudo Likelihood ; Mixed effects model ; Left censored data ; Multivariate longitudinal data
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Abstract:
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Mixed effects model based on a full likelihood is one of the few methods available to model longitudinal data subject to left-censoring. However, it is complicated algebraically due to the large dimension of the numeric computations and computationally prohibitive when the data are heavily censored. Moreover, the complexity of the computation increases as the dimension of the random effects in the model increases. We propose a method based on a pseudo likelihood function that simplifies the computational complexities, allows all possible multivariate models, including settings where the level of censoring is high. A robust variance-covariance estimator is used to adjust and correct the variance-covariance estimate. A simulation study is conducted to evaluate and compare the performance of the proposed method with existing methods.
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