title: Cumulative sojourn time in longitudinal  studies: a sequential imputation method  to handle missing health state data due  to dropout.
abstract: Missing data are ubiquitous in longitudinal studies. In this paper, we propose an imputation procedure to handle dropouts in longitudinal studies. By taking advantage of the monotone missing pattern resulting from dropouts, our imputation procedure can be carried out sequentially, which substantially reduces the computation complexity. In addition, at each step of the sequential imputation, we set up a model selection mechanism that chooses between a parametric model and a nonparametric model to impute eachmissing observation. Unlike usual model selection procedures that aim at finding a single model fitting the entire data set well, our model selection procedure is customized to find a suitable model for the prediction of each missing observation.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Li
authors.forename: Jingchen
authors.initials: J
authors.lastname: Liu
authors.forename: Naihua
authors.initials: N
authors.lastname: Duan
authors.forename: Huiping
authors.initials: H
authors.lastname: Jiang
authors.forename: Ragy
authors.initials: R
authors.lastname: Girgis
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Lieberman
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008738:Methyl Ethers; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D012107:Research Design; D000077149:Sevoflurane
chemical_list: D008738:Methyl Ethers; D000077149:Sevoflurane