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Biometrika 1999 86(1):1-13; doi:10.1093/biomet/86.1.1
© 1999 by Biometrika Trust
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A test of missing completely at random for generalised estimating equations with missing data

HY ChenA and R LittleA2

A Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60680, USA hychen@uic.edu A2 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA rlittle@umich.edu

We consider inference from generalised estimating equations when data are incomplete. A test for missing completely at random is proposed to help decide whether or not we should adjust estimating equations to correct the possible bias introduced by a missing-data mechanism that is not missing completely at random. Likelihood ratio tests have been introduced to test the missing completely at random hypothesis (Fuchs, 1982; Little, 1988). For the estimating equation setting, following the basic idea of Little (1988), we propose a Wald-type test based on an information decomposition and recombination procedure, which also provides an alternative method for estimating parameters. One application of the test is to assess the adequacy of the marginal generalised estimating equation for longitudinal data with missing values. Simulations are done to evaluate its performance.

Keywords:Drop-out; Incomplete data; Longitudinal data; Missing-data mechanism.


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