title: Administrative and artificial censoring in censored regression models.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Room 602, Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, U.S.A. mjoffe@cceb.upenn.edu
authors.forename: M M
authors.initials: MM
authors.lastname: Joffe
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000175:diagnosis / Q000401:mortality; D005260:Female; D006801:Humans; D008327:Mammography; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D008954:Models, Biological; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D016019:Survival Analysis; D016312:Treatment Refusal