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Improving Small-Sample Inference in Group Randomized Trials with Binary Outcomes

Date:
-
Location:
University of Kentucky, Whitehall Classroom Building room 204
Speaker(s) / Presenter(s):
Philip Westgate, University of Michigan

 

·     Group Randomized Trials typically randomize a small number of clusters composed of a large number of subjects, resulting in over-dispersion quantified using the intra-cluster correlation coefficient (ICC). When subject-level outcomes are binary, modeling can be done using quasi-likelihood with a logistic link. In this setting, the Wald statistic used to test for a treatment effect, which asymptotically has a standard normal distribution, may have a variance less than one, resulting in a test size smaller than its nominal value. When the ICC is known, we develop a method for adjusting the estimated standard error such that the Wald statistic approximately has a standard normal distribution. We also propose a way to resolve non-nominal test sizes when the ICC is estimated. Through simulation results covering a variety of realistic settings, we examine the performance of our methods.

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