title: Local influence in linear mixed models.
abstract: The linear mixed model has become an important tool in modelling, partially due to the introduction of the SAS procedure MIXED, which made the method widely available to practising statisticians. Its growing popularity calls for data-analytic methods to check the underlying assumptions and robustness. Here, the problem of detecting influential subjects in the context of longitudinal data is considered, following the approach of local influence proposed by Cook.
authors.affiliation: Biostatistical Centre for Clinical Trials, Catholic University of Leuven, University Hospital Sint-Rafaël, Belgium.
authors.forename: E
authors.initials: E
authors.lastname: Lesaffre
authors.forename: G
authors.initials: G
authors.lastname: Verbeke
mesh_terms: D000367:Age Factors; D001699:Biometry / Q000379:methods; D003430:Cross-Sectional Studies; D003937:Diagnosis, Differential; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D017430:Prostate-Specific Antigen / Q000097:blood; D011470:Prostatic Hyperplasia / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis*
chemical_list: D017430:Prostate-Specific Antigen