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

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.

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 645-645
Author(s):  
Dianxu Ren ◽  
Oscar Lopez ◽  
Jennifer Lingler

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.


Author(s):  
Sarwar Islam Mozumder ◽  
Mark J. Rutherford ◽  
Paul C. Lambert

In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transforming on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs simultaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival modeling approach for the cause-specific CIF. This approach models the (log cumulative) baseline hazard without requiring numerical integration, which leads to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and incorporate alternative link functions, for example, logit links. To implement these methods, we introduce a new estimation command, stpm2cr, and demonstrate useful predictions from the model through an illustrative melanoma dataset.


2019 ◽  
Vol 26 (3) ◽  
Author(s):  
Y. Hou ◽  
S. Guo ◽  
J. Lyu ◽  
Z. Lu ◽  
Z. Yang ◽  
...  

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.


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