American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



EFFICIENT AGGREGATE UNBIASED ESTIMATING FUNCTIONS APPROACH
FOR CORRELATED DATA WITH MISSING AT RANDOM

by

Annie Qu
Department of Statistics
Oregon State University


Abstract

We develop a consistent and highly efficient marginal model for missing at random data using an estimating function approach. Our approach differs from the inverse weighted estimating equations (Robins et al., 1995) and the imputation method (Paik, 1997), in that our approach does not require estimating the probability of missing or impute the missing response based on assumed models. The proposed method is based on an aggregate unbiased estimating function approach which does not require the likelihood function, however, it is equivalent to the score equation if the likelihood is known. The aggregate unbiased approach is based on a larger class of estimating functions than the pattern-unbiased approach. Therefore, the most efficient estimating function based on the aggregate unbiased approach is more efficient than pattern-unbiased approaches. We provide comparisons of the three approaches using simulated data and also an HIV data example

This is joint work with Bruce Lindsay and Lin Lu.


Date: Thursday, December 13, 2007
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room
4th Floor - Room 425
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (R627).


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