Administrative censoring, in which potential censoring times are known even for subjects who fail, is common in clinical and epidemiologic studies. Nonetheless, most statistical methods for failure-time data do not use the information contained in these potential censoring times. Robins has proposed two approaches for using this information to estimate parameters in an accelerated failure-time model; the methods generally require the analyst to treat as censored some subjects whose failure time is observed. This paper provides a rationale for this ‘artificial censoring’, discusses some of its consequences, and illustrates some of these points with data from a randomized trial of breast cancer screening. Copyright © 2001 John Wiley & Sons, Ltd.