title: Parametric models for incomplete continuous and categorical longitudinal data.
abstract: This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using several examples, these approaches are compared and contrasted. The central roles of identifiability and sensitivity are emphasized throughout.
authors.affiliation: Institute of Mathematics and Statistics, University of Kent, Canterbury, UK. m.g.kenward@ukc.ac.uk
authors.forename: M G
authors.initials: MG
authors.lastname: Kenward
authors.forename: G
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data