Title: Generalized Method of Moments Logistic Regression Model
Abstract: When analyzing longitudinal binary data, it is essential to account for both the correlation inherent from the repeated measures of the responses, as well as the correlation realized because of the feedback created between the responses at a particular time and the covariates at other times. Ignoring any of these correlations can lead to invalid conclusions. Such is the case when the covariates are time dependent and the standard logistic regression model is used. There are two types of correlations: responses with responses, and responses with covariates. We need a model that addresses both types of relationships. We postulate that there are different types of correlation presented. There is the correlation among the responses. There is the correlation between response and covariate: When responses at time t impact the covariates in time t + s; and when the covariates in time t impact the responses in time t + s. These correlations regarding feedback from Yt on to the future $$ {X}_{t+s} $$ and vice versa are important in obtaining the estimates of the regression coefficients. This chapter provides a means of modeling repeated responses with time-dependent and time-independent covariates. The coefficients are obtained using generalized method of moments. We fit these data with SAS Macro, (How to use SAS® for GMM logistic regression models for longitudinal data with time-dependent covariates (SUGI Paper 3252-2015)). Our methods are based on: Lalonde, T., Wilson, J. R., & Yin, J. (2014, November). GMM logistic regression models for longitudinal data with time-dependent covariates and extended classifications. Statistics in Medicine, 33(27).
Publication Year: 2015
Publication Date: 2015-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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