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A Test of Missing Completely at Random for Generalised Estimating Equations with Missing Data

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.