title: Inference for longitudinal data with nonignorable nonmonotone missing responses.
abstract: For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.
authors.affiliation: School of Mathematics and Statistics, Carleton University, Ottawa, ON, K1S 5B6, Canada.
authors.forename: Sanjoy K
authors.initials: SK
authors.lastname: Sinha
authors.affiliation: Stanford Genome Technology Center, Stanford, CA 94305, USA.
authors.forename: Amit
authors.initials: A
authors.lastname: Kaushal
authors.affiliation: Harvard Medical School, Boston, MA 02114, USA.
authors.forename: Wenzhong
authors.initials: W
authors.lastname: Xiao
keywords: False discovery rate; Importance sampling; Incomplete data; Linear mixed model; Longitudinal study; Maximum likelihood; Proteomics experiment