This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #307054
Title: Unconditional Estimating Equation Approach for Missing Covariates Under Ignorable Missingness
Author(s): Lin Lu*+ and Peiyong (Annie) Qu and David S. Birkes
Companies: Quintiles and University of Illinois at Urbana-Champaign and Oregon State University
Address: Biostatistics, Overland Park, KS, 66211, USA
Keywords: missing data ; GEE ; unconditional

We present an unconditional estimating equation approach to handle missing data that is missing at random. In a typical generalized estimating equation approach, covariates are generally treated as fixed variables. In our approach we treat them as random. We construct estimating equations simultaneously associated with both response and covariate variables. This enables us to handle missing covariates and consistently obtain missing information through known information. One advantage of our approach is, in contrast to the weighted estimating equation method, that it does require modeling the missing mechanism. Our approach also does not require modeling the conditional distributions that the multiple imputation method requires in order to impute missing values. In addition, it does not require fully specifying the likelihood function, but only requires the first few moments of response

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