title: Modelling non-ignorable missing-data mechanisms with item response theory models.
abstract: A model-based procedure for assessing the extent to which missing data can be ignored and handling non-ignorable missing data is presented. The procedure is based on item response theory modelling. As an example, the approach is worked out in detail in conjunction with item response data modelled using the partial credit and generalized partial credit models. Simulation studies are carried out to assess the extent to which the bias caused by ignoring the missing-data mechanism can be reduced. Finally, the feasibility of the procedure is demonstrated using data from a study to calibrate a medical disability scale.
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, Amsterdam Medical Center, The Netherlands. r.holman@amc.uva.nl
authors.forename: Rebecca
authors.initials: R
authors.lastname: Holman
authors.forename: Cees A W
authors.initials: CA
authors.lastname: Glas
mesh_terms: D000203:Activities of Daily Living / Q000145:classification; D015982:Bias; D003625:Data Collection / Q000706:statistics & numerical data; D004185:Disability Evaluation; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D011594:Psychometrics / Q000706:statistics & numerical data*