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Activity Number: 617 - Recent Developments on Order-Related Designs and Inferences
Type: Topic Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #322946 View Presentation
Title: Using Ranked Set Sampling with Binary Outcomes in Cluster Randomized Designs
Author(s): Mumu Wang* and Xinlei Wang and Johan Lim
Companies: Southern Methodist University and Southern Methodist University and Seoul National University
Keywords: generalized linear mixed model ; imperfect judgment ranking ; nonparametric inference ; relative efficiency ; maximum likelihood estimator ; pseudo likelihood estimator

We study the use of ranked set sampling (RSS) with binary outcomes in cluster randomized designs, where a generalized linear mixed model (GLMM) is used to model the hierarchical data structure involved. Under the GLMM-based framework, we develop different estimators of the treatment effect, including the nonparametric estimator (NP), maximum likelihood estimator (MLE) and pseudo likelihood estimator (PL), and study their properties and performance via numeric evaluation and/or simulation. We also develop procedures to test the existence of the treatment effect based on the three RSS estimators, and examine the power and size of the RSS tests vs. simple random sampling (SRS) tests. Further, we illustrate the proposed RSS methods with two data examples, one for rare events and the other for non-rare events. Imperfect ranking is within our consideration throughout our study. Recommendations will be given on whether to use RSS over SRS with binary outcomes in CRDs, and if yes, when to use which RSS estimator among NP, MLE and PL.

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

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