American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



HANDLING MISSING OUTCOMES WITHOUT SPECIFYING AUXILIARY MODELS
IN GENERALIZED LINEAR MIXED MODELS

by

Myunghee Paik
Columbia University


Abstract

In longitudinal studies, dropouts are known to cause analytic problems. Missing outcomes require making additional assumptions on auxiliary models that are not of primary interest, such as the missing mechanism or the conditional distribution of missing data. Since inferences rely on the correct specification of auxiliary models, a mis-specified auxiliary model may introduce bias. In this talk, we propose an estimating equation approach that does not require the auxiliary model assumptions in the generalized linear mixed models under some subclass of nonignorable missingness mechanisms. Our method is based on the pairwise conditioning of ordered outcomes. We also present a semiparametric inferential procedure without specifying the distributional assumption of outcomes.


Date: Thursday, January 25, 2007
Time: 4:00 to 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room (Room 425)
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (Room 627).


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