JSM 2004 - Toronto

Abstract #301145

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Activity Number: 82
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301145
Title: Hypothesis Testing with Clustered Binary Pairs
Author(s): Chaya S. Moskowitz*+ and Mithat Gonen and E. S. Venkatraman
Companies: Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center
Address: Department of Epidemiology and Biostatistics, New York, NY, 10021,
Keywords: design effect ; marginal homogeneity ; McNemar's test ; sensitivity ; symmetry
Abstract:

Clustered binary pairs arise naturally in many applications. For example, in the context of comparing two diagnostic tests for detection of lesions, when both tests are applied to individuals who have multiple lesions the test results form clustered binary pairs. Focusing on hypothesis testing, we propose a class of weighted test statistics and derive optimal weights that minimize the variance among this class of test statistics. We show that two existing methods, one based on the design effect and one based on the generalized score test from GEE are equal to each other and members of this class. Another method based on the intracluster correlation, while not a member of this class, is asymptotically equivalent in certain situations. We present results of simulation studies comparing the properties of the test statistics.

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