Department of Biostatistics


Prospective Students



News & Events



Employment Opportunities

Resource Quicklinks

Computing Environment




Approximating Rate of Change Under an Informative Missing Mechanism

Clayton Brown, PhD Candidate in Biostatistics

Generalized estimating equations (GEE) are formulated to approximate group mean rate of change when there is informative drop-out (right censoring.) The linear mixed model is employed to model continuous response, and extension to the generalized mixed model for discrete response is also considered. Taylor series expansions are used to approximate the first two moments to formulate quadratic estimating equations. Instead of modelling the missing data mechanism, the fixed effects are modelled as polynomial functions of drop-out time as a method to adjust for informative missingness. This work extends and builds upon the "condi- tional linear model" of Wu and Bailey (1989). The method is illustrated with data from a clinical trial of a drug for treating Schizophrenia. Simulations are also performed in the continuous case to compare various estimators, including GEE.

Return to Longitudinal/Survival Working Group List | Return to Home Page
© 2004, The Johns Hopkins University. All rights reserved.
web policies, 615 N. Wolfe Street, Batimore, MD 21205-2179, 410-955-5000