SAS Macro for Generalized Method of Moments Estimation for Longitudinal Data with Time-Dependent Covariates

Citation

Cai, Katherine & Wilson, Jeffrey (2016). SAS Macro for Generalized Method of Moments Estimation for Longitudinal Data with Time-Dependent Covariates. SAS Global Forum 2016. Las Vegas, NV.

Abstract

Longitudinal data with time-dependent covariates is not readily analyzed as there are inherent, complex correlations due to the repeated measurements on the sampling unit and the feedback process between the covariates in one time period and the response in another. A generalized method of moments (GMM) logistic regression model (Lalonde, Wilson, and Yin 2014) is one method to analyze such correlated binary data. While GMM can account for the correlation due to both of these factors, it is imperative to identify the appropriate estimating equations in the model. Cai and Wilson (2015) developed a SAS macro using SAS/IML to fit GMM logistic regression models with extended classifications. In this paper we expand the use of this macro to allow for continuous responses and as many repeated time points and predictors as possible. We demonstrate the use of the macro through two examples, one with binary response and another with continuous response.

URL

http://support.sas.com/resources/papers/proceedings16/10260-2016.pdf

Reference Type

Conference proceeding

Book Title

SAS Global Forum 2016

Author(s)

Cai, Katherine
Wilson, Jeffrey

Year Published

2016

City of Publication

Las Vegas, NV

Reference ID

9060