title: A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring.
abstract: A likelihood based approach to obtaining non-parametric estimates of the failure time distribution is developed for the copula based model of Wang et al. (Lifetime Data Anal 18:434-445, 2012) for current status data under dependent observation. Maximization of the likelihood involves a generalized pool-adjacent violators algorithm. The estimator coincides with the standard non-parametric maximum likelihood estimate under an independence model. Confidence intervals for the estimator are constructed based on a smoothed bootstrap. It is also shown that the non-parametric failure distribution is only identifiable if the copula linking the observation and failure time distributions is fully-specified. The method is illustrated on a previously analyzed tumorigenicity dataset.
authors.affiliation: Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK, a.titman@lancaster.ac.uk.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Titman
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D016001:Confidence Intervals; D005856:Germ-Free Life; D006801:Humans; D016013:Likelihood Functions; D051379:Mice; D015233:Models, Statistical; D009369:Neoplasms / Q000209:etiology