Abstract
Abstract When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces con-sistent estimates for regression parameters even if an incorrect working correlation matrix is used. However,time-varying covariates experience larger changes in coefficients than time-invariant covariates across var-ious working correlation structures for finite samples. In addition, the GEE approach may give biasedestimates under missing at random(MAR). Weighted estimating equations and multiple imputation meth-ods have been proposed to reduce biases in parameter estimates under MAR. This article studies if thetwo methods produce robust estimates across various working correlation structures for longitudinal binarydata with time-varying covariates under different missing mechanisms. Through simulation, we observe thattime-varying covariates have greater differences in parameter estimates across different working correlationstructures than time-invariant covariates. The multiple imputation method produces more robust estimatesunder any working correlation structure and smaller biases compared to the other two methods.Keywords: Generalized estimating equations, multiple imputation, weighted estimating equations, MCAR,MAR.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
