Annie Qu, Department of Statistics at the Oregon State University

Efficient aggregate unbiased estimating functions approach for correlated data with missing at random

We develop a consistent and highly efficient marginal model for missing at random data using an estimating function approach. Our
approach differs from inverse weighted estimating equations and the imputation method, 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 in pattern-unbiased approaches. We provide comparisons of the three approaches using simulated data and also an HIV data example. This is joint work with Bruce Lindsay and Lin Lu.