title: Selection models and pattern-mixture models for incomplete data with covariates.
abstract: Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.
authors: Biostatistics, Limburgs Universitair Centrum, Diepenbeek, Belgium.
authors: B
authors: B
authors: Michiels
authors: G
authors: G
authors: Molenberghs
authors: S R
authors: SR
authors: Lipsitz
mesh_terms: D001699:Biometry; D003627:Data Interpretation, Statistical; D005260:Female; D016666:Fluvoxamine / Q000009:adverse effects / Q000627:therapeutic use; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D001523:Mental Disorders / Q000188:drug therapy; D015233:Models, Statistical; D017367:Selective Serotonin Reuptake Inhibitors / Q000009:adverse effects / Q000627:therapeutic use
chemical_list: D016666:Fluvoxamine; D017367:Selective Serotonin Reuptake Inhibitors