Competing-risks model for predicting the prognosis of patients with angiosarcoma based on the SEER database of 3905 cases

Abstract

Abstract Purpose To establish a competing-risks model and compare it with traditional survival analysis, aiming to identify more precise prognostic factors for angiosarcoma. The presence of competing risks suggests that prognostic factors derived from the conventional Cox regression model may exhibit bias. Methods Patient data pertaining to angiosarcoma cases diagnosed from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate analysis employed both the Cox regression model and the Fine-Gray model, while univariate analysis utilized the cumulative incidence function and Gray’s test. Results A total of 3,905 enrolled patients diagnosed with angiosarcoma were included, out of which 2,781 succumbed to their condition: 1,888 fatalities resulted from angiosarcoma itself, and 893 were attributed to other causes. The Fine-Gray model, through multivariable analysis, identified SEER stage, gender, race, surgical status, chemotherapy status, radiotherapy status, and marital status as independent prognostic factors for angiosarcoma. The Cox regression model, due to the occurrence of competing-risk events, could not accurately estimate the effect values and yielded false-negative outcomes. Clearly, when analyzing clinical survival data with multiple endpoints, the competing-risks model demonstrates superior performance. Conclusion This current investigation may enhance clinicians’ comprehension of angiosarcoma and furnish reference data for making clinical decisions.