Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database

Kwok Keung Yim; Yishou Deng

doi:doi:10.11648/j.crj.20231102.13

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Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database

Kwok Keung Yim, Yishou Deng

Published in Cancer Research Journal (Volume 11, Issue 2)

Received: 19 April 2023 Accepted: 15 May 2023 Published: 24 May 2023

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Abstract

In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.

Published in	Cancer Research Journal (Volume 11, Issue 2)
DOI	10.11648/j.crj.20231102.13
Page(s)	49-58
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), 2023. Published by Science Publishing Group

Keywords

Thymic Carcinoma, Competing-Risks Model, SEER, Fine-Gray Model, Cause-Specific Model

References

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Cite This Article

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APA Style

Kwok Keung Yim, Yishou Deng. (2023). Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Research Journal, 11(2), 49-58. https://doi.org/10.11648/j.crj.20231102.13

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ACS Style

Kwok Keung Yim; Yishou Deng. Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Res. J. 2023, 11(2), 49-58. doi: 10.11648/j.crj.20231102.13

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AMA Style

Kwok Keung Yim, Yishou Deng. Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Res J. 2023;11(2):49-58. doi: 10.11648/j.crj.20231102.13

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@article{10.11648/j.crj.20231102.13,
  author = {Kwok Keung Yim and Yishou Deng},
  title = {Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database},
  journal = {Cancer Research Journal},
  volume = {11},
  number = {2},
  pages = {49-58},
  doi = {10.11648/j.crj.20231102.13},
  url = {https://doi.org/10.11648/j.crj.20231102.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.crj.20231102.13},
  abstract = {In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.},
 year = {2023}
}

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TY  - JOUR
T1  - Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database
AU  - Kwok Keung Yim
AU  - Yishou Deng
Y1  - 2023/05/24
PY  - 2023
N1  - https://doi.org/10.11648/j.crj.20231102.13
DO  - 10.11648/j.crj.20231102.13
T2  - Cancer Research Journal
JF  - Cancer Research Journal
JO  - Cancer Research Journal
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2330-8214
UR  - https://doi.org/10.11648/j.crj.20231102.13
AB  - In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.
VL  - 11
IS  - 2
ER  -

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Author Information

Kwok Keung Yim

Department of Rehabilitation, the First Affiliated Hospital of Jinan University, Guangzhou, China
Yishou Deng

Department of Rehabilitation, the First Affiliated Hospital of Jinan University, Guangzhou, China

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APA Style

Kwok Keung Yim, Yishou Deng. (2023). Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Research Journal, 11(2), 49-58. https://doi.org/10.11648/j.crj.20231102.13

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ACS Style

Kwok Keung Yim; Yishou Deng. Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Res. J. 2023, 11(2), 49-58. doi: 10.11648/j.crj.20231102.13

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AMA Style

Kwok Keung Yim, Yishou Deng. Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. Cancer Res J. 2023;11(2):49-58. doi: 10.11648/j.crj.20231102.13

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@article{10.11648/j.crj.20231102.13,
  author = {Kwok Keung Yim and Yishou Deng},
  title = {Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database},
  journal = {Cancer Research Journal},
  volume = {11},
  number = {2},
  pages = {49-58},
  doi = {10.11648/j.crj.20231102.13},
  url = {https://doi.org/10.11648/j.crj.20231102.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.crj.20231102.13},
  abstract = {In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database
AU  - Kwok Keung Yim
AU  - Yishou Deng
Y1  - 2023/05/24
PY  - 2023
N1  - https://doi.org/10.11648/j.crj.20231102.13
DO  - 10.11648/j.crj.20231102.13
T2  - Cancer Research Journal
JF  - Cancer Research Journal
JO  - Cancer Research Journal
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2330-8214
UR  - https://doi.org/10.11648/j.crj.20231102.13
AB  - In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.
VL  - 11
IS  - 2
ER  -

Copy | Download