Semiparametric regression analysis of clustered survival data with semi-competing risks
Analysis of semi-competing risks data is becoming increasingly important in medical research in which a subject may experience both nonterminal and terminal events, and the time to the intermediate nonterminal event (e.g. onset of a disease) is subject to dependent censoring by the terminal event (e...
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sg-ntu-dr.10356-902312023-02-28T19:24:14Z Semiparametric regression analysis of clustered survival data with semi-competing risks Peng, Mengjiao Xiang, Liming Wang, Shanshan School of Physical and Mathematical Sciences Clustered Data DRNTU::Science::Physics Copula Analysis of semi-competing risks data is becoming increasingly important in medical research in which a subject may experience both nonterminal and terminal events, and the time to the intermediate nonterminal event (e.g. onset of a disease) is subject to dependent censoring by the terminal event (e.g. death) but not vice versa. Typically, both two types of events are dependent. In many applications, subjects may also be nested within clusters, such as patients in a multi-center study, leading to possible association among event times due to unobserved shared factors across subjects. To incorporate dependency within clusters and association between two types of event times, we propose a new flexible semiparametric modeling framework where a copula model is employed for the joint distribution of the nonterminal and terminal events, and their marginal distributions are modeled by Cox proportional hazards models with random effects. A nonparametric maximum likelihood estimation procedure is developed and implemented through a Monte Carlo EM algorithm. The proposed estimator is also shown to enjoy desirable asymptotic properties. Results from extensive simulation studies indicate that the proposed method performs very well in finite samples and is especially robust against misspecification of the random effects distribution. We further illustrate the practical utility of the method by analyzing data from a multi-institutional study of breast cancer. MOE (Min. of Education, S’pore) Accepted version 2019-05-30T01:52:42Z 2019-12-06T17:43:37Z 2019-05-30T01:52:42Z 2019-12-06T17:43:37Z 2018 Journal Article Peng, M., Xiang, L., & Wang, S. (2018). Semiparametric regression analysis of clustered survival data with semi-competing risks. Computational Statistics & Data Analysis, 124, 53-70. doi:10.1016/j.csda.2018.02.003 0167-9473 https://hdl.handle.net/10356/90231 http://hdl.handle.net/10220/48475 10.1016/j.csda.2018.02.003 en Computational Statistics & Data Analysis © 2018 Elsevier B.V. All rights reserved. This paper was published in Computational Statistics & Data Analysis and is made available with permission of Elsevier B.V. 54 p. application/pdf |
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Clustered Data DRNTU::Science::Physics Copula Peng, Mengjiao Xiang, Liming Wang, Shanshan Semiparametric regression analysis of clustered survival data with semi-competing risks |
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Analysis of semi-competing risks data is becoming increasingly important in medical research in which a subject may experience both nonterminal and terminal events, and the time to the intermediate nonterminal event (e.g. onset of a disease) is subject to dependent censoring by the terminal event (e.g. death) but not vice versa. Typically, both two types of events are dependent. In many applications, subjects may also be nested within clusters, such as patients in a multi-center study, leading to possible association among event times due to unobserved shared factors across subjects. To incorporate dependency within clusters and association between two types of event times, we propose a new flexible semiparametric modeling framework where a copula model is employed for the joint distribution of the nonterminal and terminal events, and their marginal distributions are modeled by Cox proportional hazards models with random effects. A nonparametric maximum likelihood estimation procedure is developed and implemented through a Monte Carlo EM algorithm. The proposed estimator is also shown to enjoy desirable asymptotic properties. Results from extensive simulation studies indicate that the proposed method performs very well in finite samples and is especially robust against misspecification of the random effects distribution. We further illustrate the practical utility of the method by analyzing data from a multi-institutional study of breast cancer. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Peng, Mengjiao Xiang, Liming Wang, Shanshan |
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Article |
author |
Peng, Mengjiao Xiang, Liming Wang, Shanshan |
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Peng, Mengjiao |
title |
Semiparametric regression analysis of clustered survival data with semi-competing risks |
title_short |
Semiparametric regression analysis of clustered survival data with semi-competing risks |
title_full |
Semiparametric regression analysis of clustered survival data with semi-competing risks |
title_fullStr |
Semiparametric regression analysis of clustered survival data with semi-competing risks |
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Semiparametric regression analysis of clustered survival data with semi-competing risks |
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semiparametric regression analysis of clustered survival data with semi-competing risks |
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2019 |
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https://hdl.handle.net/10356/90231 http://hdl.handle.net/10220/48475 |
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1759856838981976064 |