title: Confidence Intervals for A Common Mean with Missing Data with Applications in AIDS Study.
abstract: In practical data analysis, nonresponse phenomenon frequently occurs. In this paper, we propose an empirical likelihood based confidence interval for a common mean by combining the imputed data, assuming that data are missing completely at random. Simulation studies show that such confidence intervals perform well, even the missing proportion is high. Our method is applied to an analysis of a real data set from an AIDS clinic trial study.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA, hliang@bst.rochester.edu.
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.forename: Haiyan
authors.initials: H
authors.lastname: Su
authors.forename: Guohua
authors.initials: G
authors.lastname: Zou