title: A characterization of missingness at random in a generalized shared-parameter joint modeling framework for longitudinal and time-to-event data, and sensitivity analysis.
abstract: We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time-to-event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.
authors.affiliation: I-BioStat, Universiteit Hasselt, B-3590, Diepenbeek, Belgium.
authors.forename: Edmund Njeru
authors.initials: EN
authors.lastname: Njagi
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
authors.forename: Michael G
authors.initials: MG
authors.lastname: Kenward
authors.forename: Geert
authors.initials: G
authors.lastname: Verbeke
authors.forename: Dimitris
authors.initials: D
authors.lastname: Rizopoulos
mesh_terms: D001699:Biometry / Q000379:methods*; D006801:Humans; D008103:Liver Cirrhosis / Q000188:drug therapy; D008137:Longitudinal Studies; D015233:Models, Statistical; D011241:Prednisone / Q000627:therapeutic use; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D011241:Prednisone
keywords: Censoring; Coarsening; Missing at Random; Missing not at Random; Missingness; Pattern-mixture model; Selection model; Shared-parameter model