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Nonignorable censoring in randomized clinical trials

FXB 549A, Harvard School of Public Health, 651 Huntington Ave, Boston, MA 02115, USA; jzhang{at}sdac.harvard.edu; Center for Biostatistics in AIDS Research, Harvard School of Public Health, Boston, MA 02115, USA

Daniel F Heitjan

Department of Biostatistics & Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA

Background In a clinical trial, survival may be censored by the end of the study, especially for subjects who enter later in the enrollment period. If there is a trend toward better survival over time then longer survivors experience shorter censoring times (heavier censoring). In such a case, the censoring and survival times are correlated, and thus the censoring is nonignorable in the sense that standard survival models that assume independent censoring could yield incorrect inferences. We will demonstrate a graphical method for analyzing sensitivity of estimates of survival model parameters to small departures from nonignorable censoring.

Methods We assume a parametric model of survival together with a scaled beta model for the censoring process that incorporates the dependence of censoring time on survival time. We assess sensitivity using an index of local sensitivity to nonignorability (Troxel et al. [1]). High sensitivity indicates a large impact of nonignorable censoring on the parameter of interest and a need for additional modeling.

Results A simulation study shows that the approach is valid for practical use. We apply our method to a clinical trial evaluating the survival benefit of a surgically implanted left ventricular assist device in subjects with end-stage heart failure. Sensitivity is somewhat larger in estimates of the mean survival in the device arm, where survival is better and the fraction censored is therefore larger. The degree of nonignorability required to substantially affect estimates is larger than seems plausible, however.

Conclusions Our results illustrate how one can apply sensitivity analysis to evaluate the reliability of survival parameter estimates in a clinical trial.

Clinical Trials, Vol. 2, No. 6, 488-496 (2005)
DOI: 10.1191/1740774505cn128oa


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