American Statistical Association New York City Metropolitan Area Chapter Mailman School of Public Health Columbia University Department of Biostatistics Colloquium

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