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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 Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>07/2014
<mark>Journal</mark>Lifetime Data Analysis
Issue number3
Number of pages15
Pages (from-to)444-458
Publication StatusPublished
Early online date22/06/13
<mark>Original language</mark>English


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.