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Updated: Apr 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Cancer-specific mortality and competing mortality in patients with head and neck squamous cell carcinoma: a competing
Weidong Shen1, Naoko Sakamoto, Limin Yang
1The Institute of Otolaryngology, Department of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
Background:
The objective of this study was to estimate probabilities of cancer-specific death and competing death for patients with head and neck squamous cell carcinoma (HNSCC). In addition, we attempted to construct competing risk nomograms to predict prognosis for patients with HNSCC using a large population-based cohort.
Methods:
Patients diagnosed with nonmetastatic HNSCC between 2000 and 2010 were identified from the Surveillance Epidemiology and End Results Program to form the analytic cohort. We estimated cumulative incident function (CIF) of cancer-specific mortality and competing mortality. Nomograms for predicting probability of death were built with proportional subdistribution hazard models.
Results:
The study cohort included 23,494 patients with HNSCC. The 5-year CIF for cancer-specific death and competing death were 26.7 % (95 % confidence interval [CI] 26-27.3 %) and 12.7 % (95 % CI 12.2-13.3 %), respectively; 10-year CIF were 32.8 % (95 % CI 31.9-33.6 %) and 23 % (95 % CI 22.1-24 %), respectively. On multivariate analysis, increasing cause-specific mortality was associated with increasing age, increasing tumor size, black race, single status, advanced T and N classifications, and high tumor grade. Increasing probability of competing mortality had a relationship with increasing age, male, black race, single status and nonradiotherapy. Models showed good accuracy with c-index of 0.73 for cause-specific mortality model and 0.69 for competing mortality model.
Conclusions:
We constructed competing risk nomograms for HNSCC using population-based data. The model used for building nomograms represented good performance. These nomograms can serve to guide management of patients with HNSCC.
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