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

作者：Titman Andrew C^*

来源：Lifetime Data Analysis, 2014, 20(3): 444-458.

DOI：10.1007/s10985-013-9274-4

• Non-parametric
• Current status data
• Copula
• Dependent censoring
• Sensitivity analysis

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.

• 出版日期2014-7