title: An extension of the placebo-based pattern-mixture model.
abstract: Pattern-mixture models provide a general and flexible framework for sensitivity analyses of nonignorable missing data in longitudinal studies. The placebo-based pattern-mixture model handles missing data in a transparent and clinically interpretable manner. We extend this model to include a sensitivity parameter that characterizes the gradual departure of the missing data mechanism from being missing at random toward being missing not at random under the standard placebo-based pattern-mixture model. We derive the treatment effect implied by the extended model. We propose to utilize the primary analysis based on a mixed-effects model for repeated measures to draw inference about the treatment effect under the extended placebo-based pattern-mixture model. We use simulation studies to confirm the validity of the proposed method. We apply the proposed method to a clinical study of major depressive disorders.
authors.affiliation: Forest Laboratories, Harborside Financial Center Plaza V, Jersey City, NJ, USA.
authors.forename: Kaifeng
authors.initials: K
authors.lastname: Lu
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010919:Placebos
chemical_list: D010919:Placebos
keywords: identifying restriction; missing not at random; sensitivity analysis