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Activity Number: 559
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 11:15 AM
Sponsor: Biometrics Section
Abstract #321560
Title: Improved Methods for the Marginal Analysis of Longitudinal Data in the Presence of Time-Dependent Covariates
Author(s): I-Chen Chen* and Philip Westgate
Companies: University of Kentucky and University of Kentucky
Keywords: Generalized estimating equations ; Time-dependent covariates ; Finite-sample ; Efficiency ; Longitudinal data

Generalized estimating equations (GEE) are commonly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when utilizing certain types of time-dependent covariates, these equations may not be unbiased unless the independence working correlation structure is utilized. However, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches utilizing the generalized method of moments or quadratic inference functions have been proposed in order to utilize all valid moment conditions. However, we have found that such methods will not always provide valid inference or can be improved upon in terms of finite-sample regression parameter estimation. Therefore, we propose a modified GEE approach and a method selection strategy that will ensure the validity of inference and improve regression parameter estimation. Existing and proposed methods are compared in a simulation study and application example.

Authors who are presenting talks have a * after their name.

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