Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring

We consider inference for the treatment-arm mean difference of an outcome t hat would have been measured at the end of a randomized follow-up study if, during the course of the study, patients had not initiated a nonrandomized therapy or dropped out. We argue that the treatment-arm mean difference is not identified unless unverifiable assumptions are made. We describe ident ifying assumptions that are tantamount to postulating relationships between the components of a pattern-mixture model but that can also be interpreted as imposing restrictions on the cause-specific censoring probabilities of a selection model. We then argue that, although sufficient for identificati on, these assumptions are insufficient for inference due to the curse of di mensionality. We propose reducing dimensionality by specifying semiparametr ic cause-specific selection models. These models are useful for conducting a sensitivity analysis to examine how inference for the treatment-arm mean difference changes as one varies the magnitude of the cause-specific select ion bias over a plausible range. We provide methodology for conducting such sensitivity analysis and illustrate our methods with an analysis of data f rom the AIDS Clinical Trial Group (ACTG) study 002.