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Updated: Jul 16, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma
Yingming Mu1, Junchi Luo2, Tao Xiong2
1Department of General Neurology, Ziyun Miao Buyi Autonomous County People's Hospital, Guiyang, China.
Background:
Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients.
Methods:
Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients.
Results:
The incidence of GBM has been on the rise since 1978, especially in the age group of 65-84 years. 11498 patients with GBM were included in our study. The multivariate Cox analysis revealed that age, tumor size, sex, primary tumor site, laterality, number of primary tumors, surgery, chemotherapy, radiotherapy, systematic therapy, marital status, median household income, first malignant primary indicator were independent prognostic factors of overall survival (OS) for GBMs. For cancer-specific survival (CSS), race is also independent prognostic factors. Additionally, risk of poor prognosis increased significantly with tumor size in patients with tumors smaller than 49 mm. Moreover, our nomogram model showed favorable discriminative ability.
Conclusion:
At the population level, the incidence of GBM is on the rise. The relationship between tumor size and patient prognosis is still worthy of further study. Moreover, the proposed nomogram with good performance was constructed and verified to predict the OS and CSS of patients with GBM.
