An extension of the placebo-based pattern-mixture model

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.