Abstract Details
Activity Number:
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329
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #312158
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Title:
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On Causal Inference for Population Mixtures
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Author(s):
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Pan Wu*+
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Companies:
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Keywords:
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Causal Effect ;
Non-compliance ;
Heterogeneity ;
Mixture Models ;
Structural Equation Models ;
Mental Health Study
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Abstract:
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In randomized controlled trails (RCTs), the confounding of non-compliance after initial treatment assignment is a serious problem that could lead to biased estimation of treatment effect and cause-plausible interpretation for study results. Further, it is usually inappropriate to assume the variable measuring post-treatment non-compliance follows single-mode distributions such as normal or Poisson, especially for mental health studies, since such a non-compliance variable, i.e., the amount of treatment participation, reflects the attitude of acceptance of such treatment by patients, which can be quite heterogeneous across patients. Existing approaches are unable to address such non-compliance patterns that are described by models for population mixtures. In the presentation, we would like to propose a new framework of Structural Equation Models (SEM) with robust inference to estimate the causal effect between two active treatment arms with non-compliance in each group. The proposed models are able to address the patient heterogeneity in acceptance of treatment. Instead of using likelihood based inference, the proposed methods require no assumption of parametric distribution and of
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Authors who are presenting talks have a * after their name.
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