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Adjusting for Missingness Using Summary Statistics for Repeated Measures with Random Effects and Informative Missing Data

Margaret Wu, National Institutes of Health

Different approaches to account for informative missingness in repeated measures are reviewed. We then discuss how to apply the conditional model approach (Wu and Bailey 1989 and Follmann and Wu 1995) to the setting where the probability of missing a visit depends on the random effects in a time dependent fashion. This includes the case where the probability of missing a visit depens on the true value of the primary response. Summary statistics for missingness that are weighted sums of the missing indicators are derived for these situations. These summary statistics are then incorporated as fixed effect covariates in the random effects model for the primary response. Applications of these summary statistics are then illustrated by analyzing data from real life examples.

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