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Updated: May 14, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Clinical features and prognostic nomogram development for cancer-specific death in patients with dual primary lung
Tenghao Rong1, Cheng Ai1, Tong Yang2
1Department of Cardiothoracic Surgery, Bishan Hospital of Chongqing Medical University, No. 9, Shuangxing Avenue, Bishan District, Chongqing, 402760, China.
Objective:
This study aimed to develop a concise and valid clinical prediction model to assess the survival prognostic risk of cancer-specific death in patients with dual primary lung cancer (DPLC).
Data Source:
Surveillance, epidemiology, and end results (SEER) database.
Design:
A retrospective population-based study.
Methods:
Data of DPLC patients from the database from 1992 to 2020 were collected. The number of DPLC patients was determined based on the first primary LC (FPLC) and second primary LC (SPLC), and patients were randomly assigned to a training set and a testing set in a 7:3 ratio. The primary endpoint was cancer-specific survival (CSS). Kaplan-Meier survival analysis was performed to construct survival curves. Cox analysis and bilateral stepwise regression were used to analyze prognostic factors for cancer-specific death in patients and establish the nomogram. The discriminative ability of the nomogram was assayed by C-index and calibration curves, decision-making ability was assessed by decision curve analysis (DCA), and nomogram performance was measured by receiver operating characteristic (ROC) curves.
Results:
This study included 997 DPLC patients, divided into a training set (n = 698) and a testing set (n = 299) in a 7:3 ratio. Age, gender, histological type, surgery, chemotherapy, T stage, N stage, and tumor size were identified as risk factors affecting CSS in DPLC patients (P < 0.05) and were utilized to establish a nomogram. The C-index of the nomogram in the training set was 0.671, and the AUC values of ROC curves for 1-year, 3-year, and 5-year survival rates were 0.84, 0.78, and 0.74, respectively. The C-index of the testing set was 0.644, and the AUC values were 0.72, 0.74, and 0.75, respectively. Calibration curves for both sets were close to the diagonal line, indicating good predictive ability of the nomogram. DCA curves demonstrated the good decision-making ability of the nomogram.
Conclusion:
This study revealed the clinical features of DPLC patients and developed an effective nomogram for predicting CSS, which can assist clinicians in making accurate and personalized clinical decisions regarding patient treatment.
