title: Non-parametric methods for recurrent event data with informative and non-informative censorings.
abstract: Recurrent event data are commonly encountered in health-related longitudinal studies. In this paper time-to-events models for recurrent event data are studied with non-informative and informative censorings. In statistical literature, the risk set methods have been confirmed to serve as an appropriate and efficient approach for analysing recurrent event data when censoring is non-informative. This approach produces biased results, however, when censoring is informative for the time-to-events outcome data. We compare the risk set methods with alternative non-parametric approaches which are robust subject to informative censoring. In particular, non-parametric procedures for the estimation of the cumulative occurrence rate function (CORF) and the occurrence rate function (ORF) are discussed in detail. Simulation and an analysis of data from the AIDS Link to Intravenous Experiences Cohort Study is presented.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. mcwang@jhsph.edu
authors.forename: Mei-Cheng
authors.initials: MC
authors.lastname: Wang
authors.forename: Chin-Tsang
authors.initials: CT
authors.lastname: Chiang
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000150:complications; D003198:Computer Simulation; D006760:Hospitalization; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D018709:Statistics, Nonparametric; D015819:Substance Abuse, Intravenous / Q000150:complications