STATIN trial: predictive performance of competing‐risk model in screening for pre‐eclampsia at 35–37 weeks’ gestation

2021 ◽  
Author(s):  
M. Döbert ◽  
A. Wright ◽  
A. N. Varouxaki ◽  
A. C. Mu ◽  
A. Syngelaki ◽  
...  
Keyword(s):  
Risk Model ◽  
Predictive Performance ◽  
Competing Risk ◽  
Competing Risk Model

scholarly journals Predictive performance of the competing risk model in screening for preeclampsia

2019 ◽  
Vol 220 (2) ◽  
pp. 199.e1-199.e13 ◽  
Author(s):  
David Wright ◽  
Min Yi Tan ◽  
Neil O’Gorman ◽  
Liona C. Poon ◽  
Argyro Syngelaki ◽  
...  
Keyword(s):  
Risk Model ◽  
Predictive Performance ◽  
Competing Risk ◽  
Competing Risk Model

scholarly journals Prognostic models for predicting overall and cancer-specific survival in hepatocellular carcinoma: A competing risk analysis

2020 ◽  
Author(s):  
Kongying Lin ◽  
Qizhen Huang ◽  
Zongren Ding ◽  
Yongyi Zeng ◽  
Jingfeng Liu
Keyword(s):  
Risk Model ◽  
Function Analysis ◽  
Large Population ◽  
Predictive Performance ◽  
Population Based ◽  
Competing Risk ◽  
Prognostic Models ◽  
Independent Predictive Factor ◽  
Cancer Specific Survival ◽  
Validation Set

Abstract Objectives This study was conducted to estimate the probability of cancer-specific survival (CSS) of HCC and establish a competing risk nomogram for predicting the CSS of HCC using a large population-based cohort. Methods Patients diagnosed with HCC between 2004 and 2015 were identified from the Surveillance Epidemiology and End Results Program. The CSS and overall survival (OS) were the endpoints of the study. A competing risk nomogram for predicting CSS was built with Fine and Gray’s competing risk model, and the nomogram for predicting OS was constructed with Cox proportional hazard regression models. The predictive performance of the model was tested in terms of discrimination and calibration. Results A total of 34,957 patients were included in the study and randomly divided into a training set and validation set at a ratio of 9:1. Multivariate analysis identified age, race, sex, surgical therapy, chemotherapy, radiotherapy, tumour diameter, and tumour staging as independent predictive factors of CSS. Additionally, marital status was identified as an independent predictive factor of OS. Using these factors, corresponding nomograms were constructed for CSS and OS. In the validation set, the concordance-index of the two nomogram models reached 0.810 and 0.750, respectively. Calibration curves revealed good consistency between the prediction of models and observed outcome. Furthermore, cumulative incidence function analysis and Kaplan-Meier analysis divided patients into four distinct risk subgroups, supporting the predictive performance of the models. Conclusions In this population-based analysis, we developed and validated nomograms for individualized prediction of CSS and OS in patients with HCC.

Expanded renal transplantation: a competing risk model approach

2015 ◽  
Vol 42 (12) ◽  
pp. 2539-2553
Author(s):  
Pablo Martínez-Camblor ◽  
Jacobo de Uña-Álvarez ◽  
Carmen Díaz Corte
Keyword(s):  
Renal Transplantation ◽  
Risk Model ◽  
Competing Risk ◽  
Competing Risk Model ◽  
Model Approach

Abstract P146: Comparative Effectiveness Of Direct Oral Anticoagulants Versus Warfarin Among Medicare Beneficiaries With Atrial Fibrillation

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Shirin Ardeshirrouhanifard ◽  
Huijun An ◽  
Ravi Goyal ◽  
Mukaila Raji ◽  
Caleb Alexander ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Cancer Patients ◽  
Risk Model ◽  
Oral Anticoagulants ◽  
Direct Oral Anticoagulants ◽  
Competing Risk ◽  
Secondary Outcome ◽  
Systemic Embolism ◽  
Competing Risk Model

Objective: Post-hoc analysis of three pivotal clinical trials suggests no difference in risk of ischemic stroke or systemic embolism among cancer patients with atrial fibrillation treated with direct oral anticoagulants (DOACs) vs. warfarin. However, these studies were underpowered and also do not reflect the context of real-world use. We compared the effectiveness of DOACs versus warfarin for the risk of stroke or systemic embolism and all-cause death in patients with NVAF. Methods: We used Surveillance, Epidemiology, and End Results (SEER)-Medicare data from 2009 to 2016 and included patients aged ≥66 years diagnosed with cancer (breast, bladder, colorectal, esophagus, lung, ovary, kidney, pancreas, prostate, stomach or uterus) and NVAF. We limited the cohort to patients who newly initiated warfarin or DOACs (from 2010 to 2016) with no history of ischemic stroke or systemic embolism. The primary outcome was hospitalization due to ischemic stroke or systemic embolism and the secondary outcome was all-cause death. We used Fine and Gray’s competing risk model, while treating death as a competing risk, to determine the association of oral anticoagulants with the incidence of stroke or systemic embolism. We also adjusted the analysis using inverse probability of treatment weighted (IPTW). Additionally, an IPTW-adjusted Cox proportional hazards regression model was constructed for all-cause death. Results: Of 1,028,784 patients with cancer, 158,744 (15.4%) were diagnosed with atrial fibrillation. After applying all inclusion criteria, the final study cohort included 7,334 cancer patients diagnosed with incident NVAF who newly initiated warfarin or DOACs, of which 3,194 (43.6%) used warfarin and 4,140 (56.4%) used DOACs. The unadjusted rate of stroke or systemic embolism was similar among warfarin and DOACs users (1.20 vs. 1.32 cases per 100 person-years, p=0.27). In the IPTW weighted competing risk model, the use of DOACs was not associated with an increased risk of stroke or systemic embolism compared with warfarin users (Hazard Ratio [HR] 1.41, 95% confidence intervals [CI] 0.90-2.20). However, DOACs users had a significantly lower risk of all-cause death compared with warfarin users (HR 0.82, CI 0.74-0.91). Conclusion: Among cancer patients diagnosed with NVAF, DOACs had a similar risk for stroke or systemic embolism compared to warfarin, although DOAC use was associated with reduced risk of all-cause mortality.

A competing risk model for bond strength data analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 1508-1515 ◽  
Author(s):  
Antonin Tichy ◽  
Marek Brabec ◽  
Pavel Bradna ◽  
Keiichi Hosaka ◽  
Junji Tagami
Keyword(s):  
Data Analysis ◽  
Bond Strength ◽  
Risk Model ◽  
Competing Risk ◽  
Strength Data ◽  
Competing Risk Model

A competing risk model for dependent and imperfect condition–based preventive and corrective maintenances

2014 ◽  
Vol 228 (6) ◽  
pp. 590-605 ◽  
Author(s):  
Márcio das Chagas Moura ◽  
Enrique López Droguett ◽  
Paulo Renato Alves Firmino ◽  
Ricardo José Ferreira
Keyword(s):  
Risk Model ◽  
Competing Risk ◽  
Competing Risk Model
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