The AU algorithm for estimating equations in the presence of missing data

This paper is concerned with situations in which estimating equations involve missing data and the full likelihood may not be available. We present an iterative algorithm for solving estimating equations in the presence of missing data. An application is made to a real data set from a reproductive toxicity study. Simulation results show that our method is valid and outperforms the complete-case analysis which ignores the subjects with missing data.