Introduction to Competing Risk Model in the Epidemiological Research

Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR]=0.98, P<0.001, HR=0.99, P<0.001, HR=2.66, P=0.008, and HR=0.98, P<0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model.

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Competing Risk Model in Survival Analysis

Cardiovascular Prevention and Pharmacotherapy ◽

10.36011/cpp.2020.2.e11 ◽

2020 ◽

Vol 2 (3) ◽

pp. 77

Author(s):

Yena Jeon ◽

Won Kee Lee

Keyword(s):

Survival Analysis ◽

Risk Model ◽

Competing Risk ◽

Competing Risk Model

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Impact of nonappendiceal cancer-specific death on overall survival: a competing risk analysis

Future Oncology ◽

10.2217/fon-2019-0178 ◽

2019 ◽

Vol 15 (35) ◽

pp. 4083-4093

Author(s):

Jingjing Wu ◽

Da Man ◽

Kaicen Wang ◽

Lanjuan Li

Keyword(s):

Overall Survival ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Risk Model ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Competing Risk ◽

Competing Risk Model ◽

Hazards Model ◽

Risk Of Mortality

Aim: The occurrence of nonappendiceal cancer-specific death (non-ACSD) and its impact on overall survival are unclear. Methods: Patients were extracted from the Surveillance, Epidemiology, and End Results. Results: Nearly 33.2 and 24.0% patients suffered ACSD and non-ACSD. In a Cox proportional-hazards model, unmarried patients were at greater risk of mortality than were married patients. In a competing risk model, unmarried patients were at greater risk of non-ACSD than were married patients, but the risk of ACSD did not differ significantly according to marriage status. Conclusion: The overall survival of patients with appendiceal cancer was reduced by non-ACSD. A competing risk model was more predictive of the prognosis than was a Cox proportional hazards model.

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Survival analysis of second primary malignancies after cervical cancer using a competing risk model: implications for prevention and surveillance

Annals of Translational Medicine ◽

10.21037/atm-20-2003 ◽

2021 ◽

Vol 9 (3) ◽

pp. 239-239

Author(s):

Runmei Li ◽

Yue Zhang ◽

Bingqing Ma ◽

Kangming Tan ◽

Henry S. Lynn ◽

...

Keyword(s):

Cervical Cancer ◽

Survival Analysis ◽

Risk Model ◽

Competing Risk ◽

Second Primary ◽

Competing Risk Model ◽

Second Primary Malignancies

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Cancer-Specific Survival Analysis in Patients with Gastric Cancer: Based on Competing Risk Model

10.21203/rs.3.rs-79436/v1 ◽

2020 ◽

Author(s):

Gaopei Zhu ◽

Yuhang Zhu ◽

Juan Li ◽

Weijing Meng ◽

Xiaoxuan Wang ◽

...

Keyword(s):

Gastric Cancer ◽

Survival Analysis ◽

Risk Model ◽

Competing Risk ◽

Tumor Diameter ◽

Competing Risk Model ◽

Cancer Data ◽

Risk Of Death ◽

All Cause Mortality ◽

The Impact

Abstract BackgroundCompeting risk events are prone to cause bias in the estimation of all-cause mortality. In order to eliminate the impact of competing events on survival analysis, we constructed a competing risk model. Besides, we attempted to build nomograms to predict gastric cancer-specific mortality (GCSM) and other-cause mortality (OCM).MethodsThe competing risk model was constructed to evaluate all-cause mortality, GCSM and OCM, by using the gastric cancer data from 2004 to 2013 in the Surveillance, Epidemiology, and End Results Program (SEER) dataset. Nomograms were used to predict the risk of individual dying from gastric cancer and other causes based on competing risk model.ResultsA total of 15299 cases were screened out. The 1-year, 5-year, and 8-year survival probabilities were 48.9 %, 22.1 %, and 16.4 % for all-cause mortality, respectively. Univariate and multivariate analyses showed that sex, race, marital status, age at diagnosis, malignant, tumor diameter and TNM staging were all significant prognostic factors of gastric cancer. The GCSM and OCM models showed the risk of death treated by radiotherapy decreased from 0.689 to 0.494 after considering competing risk events. Furthermore, the nomograms showed good accuracy for GCSM prediction of the 1-,5-,8-year, the AUC values of the nomograms were 0.801 [95% CI, 0.793–0.808], 0.820 [95% CI, 0.810–0.829] and 0.823 [95% CI, 0.808–0.844]. The AUC values of the nomograms for predicting 1-, 5-, and 8-year OCM were 0.784 [95% CI, 0.778–0.792], 0.755 [95% CI, 0.748–0.765] and 0.747 [95% CI, 0.739–0.759].ConclusionsOverall, the prognosis of patients with Gastric cancer is poor. The competing risk model could accurately evaluate the probability of dying from gastric cancer and other causes. Nomograms showed relatively good performance and could be considered as convenient individualized predictive tools for predicting GCSM and OCM.

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A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

Journal of Applied Econometrics ◽

10.1002/jae.2368 ◽

2014 ◽

Vol 30 (3) ◽

pp. 353-376 ◽

Cited By ~ 9

Author(s):

Martin Burda ◽

Matthew Harding ◽

Jerry Hausman

Keyword(s):

Risk Model ◽

Unobserved Heterogeneity ◽

Competing Risk ◽

Competing Risk Model ◽

Bayesian Semiparametric

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Competing risk model for predicting stabilization period of university spin-off ventures

International Entrepreneurship and Management Journal ◽

10.1007/s11365-016-0422-7 ◽

2016 ◽

Vol 13 (3) ◽

pp. 777-796 ◽

Cited By ~ 5

Author(s):

Joon Hyung Cho ◽

So Young Sohn

Keyword(s):

Risk Model ◽

Competing Risk ◽

Competing Risk Model ◽

Stabilization Period

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Expanded renal transplantation: a competing risk model approach

Journal of Applied Statistics ◽

10.1080/02664763.2015.1043866 ◽

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

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

Circulation ◽

10.1161/circ.143.suppl_1.p146 ◽

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.

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Survival analysis

Epidemiology with R ◽

10.1093/oso/9780198841326.003.0009 ◽

2020 ◽

pp. 181-218

Author(s):

Bendix Carstensen

Keyword(s):

Survival Analysis ◽

Competing Risks ◽

Cox Model ◽

Survival Function ◽

Event Analysis ◽

Time To Event ◽

Poisson Models ◽

Kaplan Meier ◽

Analysis Survival ◽

Event Rates

This chapter describes survival analysis. Survival analysis concerns data where the outcome is a length of time, namely the time from inclusion in the study (such as diagnosis of some disease) till death or some other event — hence the term 'time to event analysis', which is also used. There are two primary targets normally addressed in survival analysis: survival probabilities and event rates. The chapter then looks at the life table estimator of survival function and the Kaplan–Meier estimator of survival. It also considers the Cox model and its relationship with Poisson models, as well as the Fine–Gray approach to competing risks.

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