Online Program Home
My Program

Abstract Details

Activity Number: 618
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #321114 View Presentation
Title: Analyzing Binary Outcome Data from a Partially Clustered Design
Author(s): Brittney Bailey* and Abigail Shoben
Companies: The Ohio State University and The Ohio State University
Keywords: clinical trials ; partially clustered design ; correlated binary data ; GEE ; multilevel model
Abstract:

In a partially clustered design (PCD), individuals are randomized to one of two treatment conditions: an intervention delivered to groups created by study investigators, or a control condition where individuals remain independent. Prior research has focused on analyzing continuous outcome data from a PCD, but researchers have not evaluated approaches to analyzing binary outcome data from a PCD. We conducted a large simulation study comparing four common approaches: (1) an ordinary logistic regression model that ignores clustering; (2) a marginal model using a generalized estimating equations approach to indirectly account for clustering (3) a multilevel logistic regression model that assumes clustering occurs in both treatment conditions; (4) a multilevel logistic regression model that correctly models clustering in only one treatment condition. We evaluated the performance of each model based on the amount of bias in the estimates of the intervention effect, intracluster correlation coefficient, and type I error rate, and we made recommendations for modeling approaches based on trial size and study goals.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association