Methods for Classifying Time-Dependent Covariates in Binary Data

• Activity Number: 158
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jianqiong Yin*+
• Companies: Arizona State University
• Keywords: Correlation ; Estimating Equations ; Generalized Method of Moments ; SAS IML

Abstract

When analyzing longitudinal data it is essential to model both the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data with correct classification of time-dependent covariates provides substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation, but one must know the type of covariates. The methods presented in this paper provide measures to allow one to correctly classify covariates, so as to include the relevant estimating equations. We present the measures through an initial model for determining the estimating equations necessary with each time-dependent covariate. As an example we fit a binary GMM model using SAS IML as well as presenting the initial model to determine the classification of the covariate.