title: Conditional mixed models adjusting for non-ignorable drop-out with administrative censoring in longitudinal studies.
abstract: In this paper, a class of conditional mixed models is proposed to adjust for non-ignorable drop-out, while also accommodating unequal follow-up due to staggered entry and administrative censoring in longitudinal studies. Conditional linear and quadratic models which model subject-specific slopes as linear or quadratic functions of the time-to-drop-out, as well as pattern mixture models are both special cases of this approach. We illustrate these models and compare them with the usual maximum likelihood approach assuming ignorable drop-out using data from a multi-centre randomized clinical trial of renal disease. Simulations under various scenarios where the drop-out mechanism is ignorable and non-ignorable are employed to evaluate the performance of these models.
authors.affiliation: Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, 1050 Wishard Boulevard, RG 4101, Indianapolis, IN 46202-2872, USA. jingjinl@iupui.edu
authors.forename: Jingjin
authors.initials: J
authors.lastname: Li
authors.forename: Mark D
authors.initials: MD
authors.lastname: Schluchter
mesh_terms: D001794:Blood Pressure; D003198:Computer Simulation; D018753:Diet, Protein-Restricted / Q000592:standards; D005919:Glomerular Filtration Rate; D006801:Humans; D007674:Kidney Diseases / Q000178:diet therapy / Q000473:pathology; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015337:Multicenter Studies as Topic; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic