Administrative and artificial censoring in censored regression models

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.