title: Modified Gaussian estimation for correlated binary data.
abstract: In this paper, we develop a Gaussian estimation (GE) procedure to estimate the parameters of a regression model for correlated (longitudinal) binary response data using a working correlation matrix. A two-step iterative procedure is proposed for estimating the regression parameters by the GE method and the correlation parameters by the method of moments. Consistency properties of the estimators are discussed. A simulation study was conducted to compare 11 estimators of the regression parameters, namely, four versions of the GE, five versions of the generalized estimating equations (GEEs), and two versions of the weighted GEE. Simulations show that (i) the Gaussian estimates have the smallest mean square error and best coverage probability if the working correlation structure is correctly specified and (ii) when the working correlation structure is correctly specified, the GE and the GEE with exchangeable correlation structure perform best as opposed to when the correlation structure is misspecified.
authors.affiliation: Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
authors.forename: Xuemao
authors.initials: X
authors.lastname: Zhang
authors.forename: Sudhir
authors.initials: S
authors.lastname: Paul
mesh_terms: D000397:Air Pollution / Q000009:adverse effects; D001699:Biometry; D002648:Child; D002947:Cities / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D016011:Normal Distribution; D011634:Public Health / Q000706:statistics & numerical data; D012044:Regression Analysis
keywords: Gaussian estimation; Generalized estimating equations; Longitudinal binary data; Marginal model