Competing risk model with bivariate random effects for clustered survival data

2017 ◽  
Vol 112 ◽  
pp. 215-223 ◽  
Author(s):  
Xin Lai ◽  
Kelvin K.W. Yau ◽  
Liu Liu
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Risk Model ◽  
Competing Risk ◽  
Competing Risk Model ◽  
Clustered Survival Data

scholarly journals Competing risk between in-hospital mortality and recovery: An application of DeepHit on COVID-19 clinical data

2021 ◽  
Author(s):  
Lintu M.K. ◽  
Asha Kamath
Keyword(s):  
Deep Learning ◽  
Statistical Model ◽  
Survival Data ◽  
Risk Model ◽  
Length Of Hospital Stay ◽  
Competing Risk ◽  
Competing Risk Model ◽  
Treatment Plans ◽  
Competing Events ◽  
Novel Coronavirus

Abstract BackgroundUnexplained pneumonia appeared in Wuhan was soon determined to be a novel coronavirus disease, referred to as COVID-19. On March 11, 2020, WHO characterized COVID-19 as a pandemic. A plethora of studies on this pandemic is being carried out using various statistical and mathematical models. Though most of them are focusing on building predictive models, concentrating on the length of hospital stay can improve decision making and treatment plans. While modeling the length of stay, possible outcomes observed are either discharge or mortality.ObjectiveThe study aimed to analyse the survival data of COVID-19 patients with the competing risk methodology.MethodologyThe performance DeepHit, a deep learning-based competing risk model is compared with Fine-Gray, a traditional statistical model using a time-dependent concordance index. ResultsThe deep learning-based competing risk model outperformed the statistical model in terms of discriminative power.ConclusionModeling the duration of recovery and death provides valuable information for health officials to design proper strategies during the outbreak. These outcomes should be considered as competing events to model the data adequately.

scholarly journals A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

2014 ◽  
Vol 30 (3) ◽  
pp. 353-376 ◽  
Author(s):  
Martin Burda ◽  
Matthew Harding ◽  
Jerry Hausman
Keyword(s):  
Risk Model ◽  
Unobserved Heterogeneity ◽  
Competing Risk ◽  
Competing Risk Model ◽  
Bayesian Semiparametric

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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