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Activity Number: 242
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #308643
Title: Inference for Treatment Effects in Clinical Trials with Nonrandom Dropouts
Author(s): Shan Kang*+ and Roderick J. Little
Companies: University of Michigan and University of Michigan
Keywords: Clinical Trials ; Dropouts ; Missing at Random ; Likelihood Inference
Abstract:

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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