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Activity Number:
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498
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309603 |
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Title:
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A Joint Modeling Approach for Analyzing Nonignorable Missing Data
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Author(s):
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Sujit Ghosh*+ and Liansheng Zhu
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Companies:
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North Carolina State University and Pharmaceutical Product Development, Inc
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Address:
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2501 Founders Drive, Raleigh, NC, 27695-8203,
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Keywords:
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longitudinal data ; joint modeling ; mcmc ; nonignorable missing ; clinical trial
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Abstract:
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In longitudinal studies, data are often missing and when the missing mechanism is informative and hence not ignorable, it is generally difficult to analyze such non-ignorable missing (NIM) data since the distributional assumptions about missing data are not easily verifiable. Within the class of pattern-mixture models we develop a joint-modeling (JM) approach, in which patterns considered as random effects are marginalized within a generalized linear mixed model framework. The JM approach is shown to be able to capture the dependence of missing indicators on missing outcomes in some degree as is the case with NIM data. Some of the main advantages of the proposed approach include (i) the capability to handle both continuous and discrete responses, (ii) avoidance of the problem of under-identifiability, (iii) availability of marginal estimates, and (iv) computational efficiency.
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