Background:The relationship between the regression and prognosis of melanoma has been debated for years.When competing-risk events are present,using traditional survival analysis methods may induce bias in the identif...Background:The relationship between the regression and prognosis of melanoma has been debated for years.When competing-risk events are present,using traditional survival analysis methods may induce bias in the identified prognostic factors that affect patients with regressive melanoma.Methods:Data on patients diagnosed with regressive melanoma were extracted from the Surveillance,Epidemiology,and End Results(SEER)database during 2000-2019.Cumulative incidence function and Gray's test were used for the univariate analysis,and the Cox proportional-hazards model and the Fine-Gray model were used for the multivariate analysis.Results:A total of 1442 eligible patients were diagnosed with regressive melanoma,including 529 patients who died:109 from regressive melanoma and 420 from other causes.The multivariate analysis using the Fine-Gray model revealed that SEER stage,surgery status,and marital status were important factors that affected the prognosis of regressive melanoma.Due to the existence of competing-risk events,the Cox model may have induced biases in estimating the effect values,and the competing-risks model was more advantageous in the analysis of multipleendpoint clinical survival data.Conclusion:The findings of this study may help clinicians to better understand regressive melanoma and provide reference data for clinical decisions.展开更多
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 prognost...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 Sur-veillance,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 suc-cumbed 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 refer-ence data for making clinical decisions.展开更多
基金Key Scientific Problems and Medical Technical Problems Research Project of China Medical Education Association(2022KTZ009)Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization(2021B1212040007).
文摘Background:The relationship between the regression and prognosis of melanoma has been debated for years.When competing-risk events are present,using traditional survival analysis methods may induce bias in the identified prognostic factors that affect patients with regressive melanoma.Methods:Data on patients diagnosed with regressive melanoma were extracted from the Surveillance,Epidemiology,and End Results(SEER)database during 2000-2019.Cumulative incidence function and Gray's test were used for the univariate analysis,and the Cox proportional-hazards model and the Fine-Gray model were used for the multivariate analysis.Results:A total of 1442 eligible patients were diagnosed with regressive melanoma,including 529 patients who died:109 from regressive melanoma and 420 from other causes.The multivariate analysis using the Fine-Gray model revealed that SEER stage,surgery status,and marital status were important factors that affected the prognosis of regressive melanoma.Due to the existence of competing-risk events,the Cox model may have induced biases in estimating the effect values,and the competing-risks model was more advantageous in the analysis of multipleendpoint clinical survival data.Conclusion:The findings of this study may help clinicians to better understand regressive melanoma and provide reference data for clinical decisions.
基金supported by Key Scientific Problems and Medical Technical Problems Research Project of China Medical Education Association[2022KTZ009]Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization[2021B1212040007].
文摘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 Sur-veillance,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 suc-cumbed 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 refer-ence data for making clinical decisions.
