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 that 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 identifying 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 identification, these assumptions are insufficient for inference due to the curse of dimensionality. We propose reducing dimensionality by specifying semiparametric 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 selection bias over a plausible range. We provide methodology for conducting such sensitivity analysis and illustrate our methods with an analysis of data from the AIDS Clinical Trial Group (ACTG) study 002.
Rotnitzky
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A
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Scharfstein
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D
Su
T L
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Robins
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J
D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy; D019380:Anti-HIV Agents / Q000008:administration & dosage; D001699:Biometry; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D015215:Zidovudine / Q000008:administration & dosage
D019380:Anti-HIV Agents; D015215:Zidovudine