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Abstract Details

Activity Number: 244
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #305120
Title: Bias with Differential Dropout: What Matters and What Doesn't
Author(s): Melanie Bell*+
Companies: University of Sydney
Address: PoCoG, Univerisity of Sydney, International, 2006, Australia
Keywords: Missing data ; Longitudinal analysis ; mixed models
Abstract:

Background: Dropout in longitudinal randomized trials is common, and threatens validity of results. When dropout rates differ between treatment arms it is sometimes called differential dropout. The implications of differential dropout are often misunderstood: some believe that if dropout rates are similar between study arms estimated treatment effects will be unbiased. Or, if differential dropout occurs, results are biased. This study demonstrates that these beliefs are false. Methods: Data were simulated for a randomized 2 arm trial of 3 time points which were complete, missing completely at random, at random, and not at random (MNAR). Missingness was 30% in the equal case, and 40% and 20% for unequal missingness. Data were analyzed with a t-test and simple approaches to missing data; and a mixed model contrast. Results: The simple approaches yielded biased estimates of the treatment effect, for nearly every type of dropout and missing data type. Mixed models yielded unbiased estimates for all scenarios except when data were MNAR. Conclusions: Bias depends on the type of missingness and the analysis, not whether the dropout rates are the same between groups.


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