A competing risk analysis of hormone therapy interruption in Asian women with breast cancer

2014 ◽  
Vol 24 (3) ◽  
pp. 301-309 ◽  
Author(s):  
Kun-Pin Hsieh ◽  
Li-Chia Chen ◽  
Kwok-Leung Cheung ◽  
Yi-Hsin Yang
BMC Cancer ◽  
2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Wahyu Wulaningsih ◽  
Mariam Vahdaninia ◽  
Mark Rowley ◽  
Lars Holmberg ◽  
Hans Garmo ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
pp. 10-17
Author(s):  
Tian Lan ◽  
Yunyan Lu ◽  
Hua Luo ◽  
Ouou Yang ◽  
Junling He ◽  
...  

2019 ◽  
Author(s):  
Yanbo Xu ◽  
Hong Liu ◽  
Qi-Hua Cao ◽  
Jia-Li Ji ◽  
Rong-Rong Dong ◽  
...  

Abstract BackgroundPatients with early stage breast cancer (BC) live long but accompany with competing comorbidities. This study aims to estimate the impact of cancer and non-cancer causes of death in early stage BC patients, and further quantify the survival differences. MethodsPatients diagnosed with breast cancer between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database were enrolled in the study. Cumulative incidence function (CIF) for cause-specific death and other causes of death were estimated, and the differences were tested by Gray’s test. Nomogram for estimating 3-, 4-, and 5-year overall survival, cancer-specific survival and other-cause-specific survival was established based on Cox regression analysis and Fine and Gray’s competing risk analysis. The discriminative ability, calibration and precision of the nomogram was evaluated and compared using C statistics, calibration plots and the area under receiver operating characteristic curve. Results196304 eligible patients with early-stage BC patients were enrolled in this study. Prolonged overall survival (OS) was associated with younger age, well differentiation, smaller tumor size, Luminal subtype and presence of surgery ( p <0.001). For competing events, Fine and Gray's competing risk analysis was used to validate the predictors: i ncreasing age, poorer differentiation, larger tumor size, triple negative subtype, HER2 enriched subtype and absence of surgery for cancer-specific mortality (CSM); and increasing age, larger tumor size and absence of surgery for other-cause-specific mortality (OCSM). The established nomogram was well calibrated, and displayed good discrimination in both training cohort and validation cohort by calibration plots (Figure 4), with a concordance index of 0.801 (95% CI, 0.795-0.806; p =0.003) for OS prediction; 0.830 (95% CI, 0.824-0.836; p =0.003) for CSM prediction and 0.806 (95% CI, 0.798-0.814; p =0.004) for OCSM prediction. Furthermore, the AUC values for predicting survival and death were: OS, 80.2% (3-year), 79.5% (4-year), and 78.7% (5-year); CSM, 83.0% (3-year), 81.7% (4-year), and 80.3% (5-year); OCSM 81.3% (3-year), 80.8% (4-year), and 81.7% (5-year) (Figure 5). ConclusionsWe evaluated OS and CIF of cancer-specific death and other-cause-specific death in patients with early stage BC based on Cox regression analysis and Fine and Gray’s competing risk analysis and developed the first comprehensive prognostic nomogram.


2019 ◽  
Vol 10 (3) ◽  
pp. 583-593 ◽  
Author(s):  
Wei Sun ◽  
Minghua Cheng ◽  
Huaqiang Zhou ◽  
Wenqi Huang ◽  
Zeting Qiu

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Rakesh Kumar Saroj ◽  
K. Narasimha Murthy ◽  
Mukesh Kumar ◽  
Atanu Bhattacharjee ◽  
Kamalesh Kumar Patel

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