Abstract Details
Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #308643 |
Title:
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Inference for Treatment Effects in Clinical Trials with Nonrandom Dropouts
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Author(s):
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Shan Kang*+ and Roderick J. Little
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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Clinical Trials ;
Dropouts ;
Missing at Random ;
Likelihood Inference
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Abstract:
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Rubin (1976) showed that the missing data mechanism can be ignored for likelihood-based inference about parameters when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values after conditioning on the observed data, and (b) the parameters of the data model and the missing-data mechanism are distinct. However, for some missing not at random (MNAR) mechanisms, valid inferences can be obtained for subsets of the parameters without modeling the mechanism. Formalizing this idea, Little and Zanganeh (2013) proposed weaker definitions of MAR and ignorability for parameter subsets. We apply this idea to dropout in clinical trials, where dropout depends on side-effects and outcomes, but not necessarily the treatment assigned. Such a mechanism is particularly plausible in single-blind studies where the participant does not know which treatment is assigned. With a categorical outcome variable, we show how valid treatment effects can be estimated from MNAR data without relying on a model for the missing data mechanism.
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Authors who are presenting talks have a * after their name.
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