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A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring

Research output: Contribution to journalJournal article

Published

Journal publication date2014
JournalLifetime Data Analysis
Early online date22/06/13
Original languageEnglish

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 Analysis, 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.