You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Estimating Equations for Measures of Association between Repeated Binary Responses
Stuart R. Lipsitz and Garrett M. Fitzmaurice
Vol. 52, No. 3 (Sep., 1996), pp. 903-912
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2533051
Page Count: 10
You can always find the topics here!Topics: Estimators, Maximum likelihood estimation, Correlations, Statistical estimation, Children, Consistent estimators, Longitudinal studies, Biometrics, Missing data, Matrices
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Moment-based methods for analyzing repeated binary responses using the marginal odds ratio as a measure of association have been proposed by a number of authors. Carey, Zeger, and Diggle (1993, Biometrika 80, 517-526) have recently described how the marginal odds ratio can be estimated using generalized estimating equations (GEE) based on conditional residuals (deviations about conditional expectations). In this paper, we show that other measures of association between pairs of binary responses, e.g., the correlation, can also be estimated using conditional residuals. We demonstrate that the estimator of the correlation based on conditional residuals is nearly efficient when compared with maximum likelihood or second order estimating equations (GEE2) except when the correlation is large. This estimator also yields more efficient estimates of the correlation than the usual GEE estimator that is based on unconditional residuals. Furthermore, the gains in efficiency can be quite considerable when some of the responses are missing or incomplete, or, alternatively, when cluster sizes are unequal (in the clustered data setting).
Biometrics © 1996 International Biometric Society