Application of survival analysis method for the examination of unemployment leaving forms

2017 ◽  
Vol 62 (8) ◽  
pp. 5-18
Author(s):  
Beata Bieszk-Stolorz
Keyword(s):  
Survival Analysis ◽  
Conditional Probability ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Function ◽  
Analysis Method ◽  
Incidence Function ◽  
Kaplan Meier

The purpose of this article is to present selected methods of the survival analysis to evaluate the probability of leaving unemployment for the various types of competing risks. Complement to the unity of the Kaplan-Meier estimator, cumulative incidence function and cumulative conditional probability were used in the study. With these three estimators, the probability of deregistering caused by undertaking work, refusal and other causes were compared. The analysis was based on data from the Powiat Labour Office in Szczecin.

scholarly journals P1507COMPETING RISKS MODELING IN PERITONEAL DIALYSIS: THE URGE FOR A CHANGE IN SURVIVAL ANALYSIS METHODOLOGY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Anabela Malho Guedes ◽  
Roberto Calças ◽  
Ana Domingos ◽  
Teresa Jerónimo ◽  
Pedro Neves ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Peritoneal Dialysis ◽  
Renal Transplantation ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Function ◽  
Charlson Index ◽  
Risk Of Death ◽  
Incidence Function ◽  
Kaplan Meier

Abstract Background and Aims Survival analysis is a cornerstone in medical research. For this purpose Kaplan-Meier is the most widely used statistical test, but the presence of competing risks violates the fundamental assumption that the censoring mechanism is independent of survival time. This leads to overestimation of the cumulative probability of cause-specific failure. Cumulative incidence estimate and competing risks analysis are preferred. The purpose of this study was to compare different survival analysis methods: Kaplan-Meier and cumulative incidence function estimates in a cohort of Peritoneal Dialysis (PD) patients. Method The survival of 115 incident patients on PD in a university hospital was evaluated after establishing 2 cohorts: patients starting renal replacement therapy with PD (PD first; n=85) and patients switching to PD on the first 6 months of dialysis (PD transfer; n=30). Kaplan-Meier, cumulative incidence function, cause-specific and subdistribution hazards were performed. The event of interest was death and the competing risk events were transfer to hemodialysis and renal transplantation. Results Besides higher residual renal function (RRF) and kt/V in the PD first group, there were no other significant differences between groups. There were 22 deaths. PD first group had a better survival with both Kaplan-Meier (log-rank test, p=0.013) and cumulative incidence function (p=0.021) approaches. The Cox regression model showed, as protecting variables, higher albumin (HR=0.174; CI95% 0.054-0.562), higher RRF (HR=0.785; CI95% 0.666-0.925) and PD first (HR=0.350; CI95% 0.132-0.927). Higher Charlson Index predicted worse outcome (HR=1.459; CI95% 1.159-1.835). PD as first dialysis therapy was associated with 65.0 % lower risk of death comparing with PD transfer. The subdistribution multivariable model found higher Charlson Index (HR=1.389; CI95% 1.118-1.725) and lower RRF (HR=0.798; CI95% 0.680-0.936) were statistically associated with death, but not PD transfer or albumin. This result differs from the obtained using the cause-specific hazard model. Analyzing the competing events, patients submitted to renal transplantation had a lower Charlson Index. Conclusion The probability of death was overestimated by the Kaplan-Meier method. The bias of Kaplan-Meier is especially great when the hazard of the competing risks is large. This study consisted on a statistical critical analysis of a real medical example, broader clinical conclusions related with “PD first initiative” should be cautious in this context. It is primordial to recognize the presence of competing risks in studies with multiple outcomes, as in Peritoneal Dialysis studies, to estimate cumulative incidence and yield more accurate results. This study shows how different conclusions are attained with different statistical methodology and its relevance in clinical context.

scholarly journals Prognostic factors in Asian and white American patients with cervical cancer, considering competing risks

2019 ◽  
Vol 26 (3) ◽  
Author(s):  
Y. Hou ◽  
S. Guo ◽  
J. Lyu ◽  
Z. Lu ◽  
Z. Yang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prognostic Factors ◽  
Competing Risks ◽  
Asian American ◽  
Cumulative Incidence ◽  
Figo Stage ◽  
Cumulative Incidence Function ◽  
White American ◽  
Node Metastasis ◽  
Incidence Function

Background Cervical cancer is the 2nd most common malignant tumour in women worldwide. Previous research studies have given little attention to its prognostic factors in the rapidly growing Asian American population. In the present study, we explored prognostic factors in Asian and white American patients with cervical cancer, considering competing risks.Methods The study included 58,780 patients with cervical cancer, of whom 54,827 were white and 3953 were Asian American, and for all of whom complete clinical information was available in the U.S. Surveillance, Epidemiology, and End Results database. Death from cervical cancer was considered to be the event of interest, and deaths from other causes were defined as competing risks. The cumulative incidence function and the Fine–Gray method were applied for univariate and multivariate analysis respectively.Results We found that, for all patients (white and Asian American combined), the cumulative incidence function was associated with several factors, such as age at diagnosis, figo (Fédération internationale de Gynécologie et d’Obstétrique) stage, registry area, and lymph node metastasis. Similar results were found when considering white patients only. However, for Asian American patients, registry area was not associated with the cumulative incidence function, but the other factors (for example, figo stage) remained statistically significant. Similarly, in multivariate analyses, we found that age at diagnosis, figo stage, lymph node metastasis, tumour histology, treatment method, and race were all associated with prognosis.Conclusions Survival status differs for white and Asian American patients with cervical cancer. Our results could guide the treatment of, and facilitate prognostic judgments about, white and Asian American patients with cervical cancer.

scholarly journals Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework

2020 ◽  
Vol 49 (3) ◽  
pp. 25-29
Author(s):  
Yosra Yousif ◽  
Faiz Ahmed Mohamed Elfaki ◽  
Meftah Hrairi
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Model Simulation ◽  
Cumulative Incidence Function ◽  
Subdistribution Hazard ◽  
Incidence Function ◽  
Proposed Model ◽  
Competing Risks Data ◽  
Cause Of Failure ◽  
Proportional Subdistribution Hazard

In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.

scholarly journals Analysis of Survival Data with Competing Risks in ADRD (Alzheimer's Disease and Related Dementias) Research

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 645-645
Author(s):  
Dianxu Ren ◽  
Oscar Lopez ◽  
Jennifer Lingler
Keyword(s):  
At Risk ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Hazard Model ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Subdistribution Hazard ◽  
Incidence Function ◽  
Diabetes Status ◽  
Conventional Methods

Abstract Competing risk is an event that precludes the occurrence of the primary event of interest. For example, when studying risk factors associated with dementia, death before the onset of dementia serve as a competing event. A subject who dies is no longer at risk of dementia. This issue play more important role in ADRD research given the elderly population. Conventional methods for survival analysis assume independent censoring and ignore the competing events. However, there are some challenge issues using those conventional methods in the presence of competing risks. First, no one-to-one link between hazard function and cumulative incidence function (CIF), and Kaplan-Meier approach overestimates the cumulative incidence of the event of interest. Second, the effect of covariates on hazard rate cannot be directly linked to the effect of cumulative incidence (the risk). We will discuss two types of analyses in the presence of competing risk: Cause-specific hazard model and Fine-Gray subdistribution hazard model. Cause-specific hazard model directly quantify the cause-specific hazard among subjects who are at risk of developing the event of interest, while Fine-Gray subdistribution hazard model directly model the effects of covariates on the cumulative incidence function. The type of research questions (Association vs. Prediction) may guide the choice of different statistical approaches. We will illustrate those two competing risk analyses using the large national dataset from National Alzheimer’s Coordinating Center (NACC). We will analyze the association between baseline diabetes status and the incidence of dementia, in which death before the onset of dementia is a competing event.

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