Current status data occurs when failure time of subjects in a survival study is only known to be either less or greater than the censoring time. Thus, the failure time is either left – or right – censored. Analyzing data of this structure under the Cox Proportional Hazards model with dependent censoring assumption can be challenging. To address this, a Penalized Maximum Likelihood Estimation (PMLE) approach was proposed. The unknown baseline cumulative hazard functions for both the failure time and the censoring time were estimated using splines. The advantage of penalized approach over unpenalized method is that that the desired smoothness level of the functions are controlled by their respective penalty terms. The possible dependence between the failure and censoring times was accounted for using the gamma-frailty model. An easy to implement hybrid computational algorithm is proposed to estimate the PMLEs and the Bayesian technique was employed for the estimation of the variances of the parameters. Extensive simulation studies were conducted to assess the statistical properties of the PMLEs. It was observed that the realized estimators were not only consistent, asymptotically normal and efficient, but also, were robust to the number of knots chosen, the proportion of dependent censoring used and the frailty distribution employed. The proposed PMLE method was further applied to real data obtained from tumorigenicity experiment.
| Published in | Biomedical Statistics and Informatics (Volume 9, Issue 3) |
| DOI | 10.11648/j.bsi.20240903.11 |
| Page(s) | 39-57 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Current Status Data, Splines, Proportional Hazards Model, Penalized Maximum Likelihood Estimation, Frailty Model
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APA Style
Faisal, A., Iddi, S., Nortey, E. N. N., Doku-Amponsah, K. (2024). Penalized Likelihood Estimation for Current Status Data with Informative Censoring. Biomedical Statistics and Informatics, 9(3), 39-57. https://doi.org/10.11648/j.bsi.20240903.11
ACS Style
Faisal, A.; Iddi, S.; Nortey, E. N. N.; Doku-Amponsah, K. Penalized Likelihood Estimation for Current Status Data with Informative Censoring. Biomed. Stat. Inform. 2024, 9(3), 39-57. doi: 10.11648/j.bsi.20240903.11
AMA Style
Faisal A, Iddi S, Nortey ENN, Doku-Amponsah K. Penalized Likelihood Estimation for Current Status Data with Informative Censoring. Biomed Stat Inform. 2024;9(3):39-57. doi: 10.11648/j.bsi.20240903.11
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Download@article{10.11648/j.bsi.20240903.11,
author = {Alhassan Faisal and Samuel Iddi and Ezekiel Nii Noye Nortey and Kwabena Doku-Amponsah},
title = {Penalized Likelihood Estimation for Current Status Data with Informative Censoring},
journal = {Biomedical Statistics and Informatics},
volume = {9},
number = {3},
pages = {39-57},
doi = {10.11648/j.bsi.20240903.11},
url = {https://doi.org/10.11648/j.bsi.20240903.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20240903.11},
abstract = {Current status data occurs when failure time of subjects in a survival study is only known to be either less or greater than the censoring time. Thus, the failure time is either left – or right – censored. Analyzing data of this structure under the Cox Proportional Hazards model with dependent censoring assumption can be challenging. To address this, a Penalized Maximum Likelihood Estimation (PMLE) approach was proposed. The unknown baseline cumulative hazard functions for both the failure time and the censoring time were estimated using splines. The advantage of penalized approach over unpenalized method is that that the desired smoothness level of the functions are controlled by their respective penalty terms. The possible dependence between the failure and censoring times was accounted for using the gamma-frailty model. An easy to implement hybrid computational algorithm is proposed to estimate the PMLEs and the Bayesian technique was employed for the estimation of the variances of the parameters. Extensive simulation studies were conducted to assess the statistical properties of the PMLEs. It was observed that the realized estimators were not only consistent, asymptotically normal and efficient, but also, were robust to the number of knots chosen, the proportion of dependent censoring used and the frailty distribution employed. The proposed PMLE method was further applied to real data obtained from tumorigenicity experiment.},
year = {2024}
}
TY - JOUR T1 - Penalized Likelihood Estimation for Current Status Data with Informative Censoring AU - Alhassan Faisal AU - Samuel Iddi AU - Ezekiel Nii Noye Nortey AU - Kwabena Doku-Amponsah Y1 - 2024/10/29 PY - 2024 N1 - https://doi.org/10.11648/j.bsi.20240903.11 DO - 10.11648/j.bsi.20240903.11 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 39 EP - 57 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20240903.11 AB - Current status data occurs when failure time of subjects in a survival study is only known to be either less or greater than the censoring time. Thus, the failure time is either left – or right – censored. Analyzing data of this structure under the Cox Proportional Hazards model with dependent censoring assumption can be challenging. To address this, a Penalized Maximum Likelihood Estimation (PMLE) approach was proposed. The unknown baseline cumulative hazard functions for both the failure time and the censoring time were estimated using splines. The advantage of penalized approach over unpenalized method is that that the desired smoothness level of the functions are controlled by their respective penalty terms. The possible dependence between the failure and censoring times was accounted for using the gamma-frailty model. An easy to implement hybrid computational algorithm is proposed to estimate the PMLEs and the Bayesian technique was employed for the estimation of the variances of the parameters. Extensive simulation studies were conducted to assess the statistical properties of the PMLEs. It was observed that the realized estimators were not only consistent, asymptotically normal and efficient, but also, were robust to the number of knots chosen, the proportion of dependent censoring used and the frailty distribution employed. The proposed PMLE method was further applied to real data obtained from tumorigenicity experiment. VL - 9 IS - 3 ER -
Department of Statistics, Faculty of Physical Sciences, University for Development Studies, Tamale, Ghana
Department of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra
Department of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra
Department of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra
