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Updated: Nov 23, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Prognostic risk factors for initially diagnosed T2N0M0 glottic cancer: competing risk analysis and propensity-score
Guan-Jiang Huang1, Bei-Bei Yang2
1Department of Otorhinolaryngology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
Background:
Prognostic risk factors of patients with initially diagnosed T2N0M0 glottic cancer remain unclear. This study was aimed to conduct a comprehensive analysis to identify valuable prognostic risk factors for initially diagnosed T2N0M0 glottic cancer.
Methods:
Data of patients with initially diagnosed T2N0M0 glottic cancer were extracted from the Surveillance, Epidemiology, and End Results database. Survival analyses and Cox regression analyses were conducted to evaluate overall survival (OS) and cancer-specific survival (CSS). In consideration of competing events, the competing risk (CR) analysis was applied. Furthermore, propensity-score matching (PSM) was applied to mimic randomized-controlled trials and reduce selection bias.
Results:
A total of 923 eligible patients met the inclusion criteria. Survival analyses showed that age, marital status, primary site surgery, and radiation were independent predictors of OS. Besides, age, marital status, primary site surgery, radiation, and chemotherapy were independent predictors of CSS. Cox regression analyses and the CR analysis were basically consistent with this result. In addition, an internal validation and PSM were performed to explore the role of chemotherapy.
Conclusion:
We conducted a comprehensive analysis to prove that age, marital status, primary site surgery, radiation, and chemotherapy may be valuable prognostic risk factors for initially diagnosed T2N0M0 glottic cancer. Primary site surgery and radiation should be recommended, whereas chemotherapy was likely not suitable so far. Furthermore, we constructed a CR nomogram to predict survival rates.
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