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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/Magazine › Journal article › peer-review

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A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring. / Titman, Andrew.
In: Lifetime Data Analysis, Vol. 20, No. 3, 07.2014, p. 444-458.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Titman, A 2014, 'A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring', Lifetime Data Analysis, vol. 20, no. 3, pp. 444-458. https://doi.org/10.1007/s10985-013-9274-4

APA

Titman, A. (2014). A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring. Lifetime Data Analysis, 20(3), 444-458. https://doi.org/10.1007/s10985-013-9274-4

Vancouver

Titman A. A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring. Lifetime Data Analysis. 2014 Jul;20(3):444-458. Epub 2013 Jun 22. doi: 10.1007/s10985-013-9274-4

Author

Titman, Andrew. / A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring. In: Lifetime Data Analysis. 2014 ; Vol. 20, No. 3. pp. 444-458.

Bibtex

@article{296f7b8587dc467a927d3136c96c1cc4,
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 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.",
keywords = "non-parametric, current status data, copula, dependent censoring, Sensitivity analysis",
author = "Andrew Titman",
year = "2014",
month = jul,
doi = "10.1007/s10985-013-9274-4",
language = "English",
volume = "20",
pages = "444--458",
journal = "Lifetime Data Analysis",
issn = "1380-7870",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring

AU - Titman, Andrew

PY - 2014/7

Y1 - 2014/7

N2 - 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.

AB - 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.

KW - non-parametric

KW - current status data

KW - copula

KW - dependent censoring

KW - Sensitivity analysis

U2 - 10.1007/s10985-013-9274-4

DO - 10.1007/s10985-013-9274-4

M3 - Journal article

VL - 20

SP - 444

EP - 458

JO - Lifetime Data Analysis

JF - Lifetime Data Analysis

SN - 1380-7870

IS - 3

ER -