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Updated: Aug 6, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Competing Risk Model to Determine the Prognostic Factors for Patients with Gliosarcoma
Mingyi Chen1, Liying Huang2, Fang Wang1
1Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Background:
Gliosarcoma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods because of a lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors.
Methods:
For this study, we extracted data of patients diagnosed with GSM from the SEER (Surveillance Epidemiology and End Results) database between 2000 and 2019. To account for the influence of competing risks, we used a Cumulative Incidence Function. Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for patients with GSM, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. In addition, we used both univariate and multivariate Cox analysis to account for non-GSM-related deaths that may confound our research.
Results:
The competing risk model showed that age, marital status, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60-70 years, ≥71 years) and larger tumor size (≥5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy, and being single were identified as protective factors against GSM-related death.
Conclusions:
Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the use of protective measures may contribute to improved outcomes and reduced mortality for patients with GSM.
