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Activity Number:
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58
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309049 |
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Title:
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Modeling Common Effects of Predictors on Multiple Longitudinal Outcomes
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Author(s):
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Juan Jia*+ and Robert E. Weiss
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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Department of Biostatistics, School of Public Health, Los Angeles, CA, 90025,
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Keywords:
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Multivariate longitudinal data ; Bayesian inference ; common effect
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Abstract:
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Longitudinal data with multivariate outcomes measured over time are common in medical, psychological and sociological fields. In typical models, each covariate has a different effect on each outcome. However, outcomes are often quite similar and covariate effects might be expected to be similar as well. Lin et al. (2000) estimated a common effect for single covariate. We instead propose a model to evaluate a common effect for the entire linear predictor. Maximum a posteriori inference with a flat prior is used to estimate model parameters. We apply the proposed method to the Brief Symptom Inventory (BSI) data from children of HIV+ parents. We estimate common effects of age, gender, and parental drug use on nine sub-scales.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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