What are competing risk models?
What are competing risk models?
Competing risk analysis refers to a special type of survival analysis that aims to correctly estimate marginal probability of an event in the presence of competing events.
What do you mean by competing risk?
A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk.
What is fine and gray method?
The Fine and Gray method provides a way to introduce covariate information into those predictions, potentially making them more accurate for individual patients. It’s important to note, however, that one can also calculate cumulative incidence functions based on cause-specific hazard functions.
What is fine and gray regression model?
To overcome this limitation, Fine and Gray  developed a survival regression model based on cumulative incidence function (CIF) which describes the probability of occurring an event prior to a specific time. In the same section, Fine-Gray competing risk regression model is briefly described.
Is cumulative incidence 1 survival?
In other words, the cumulative incidence of an event at a given time is one minus the overall survival probability at that time. An investigator may be interested in examining outcomes other than mortality, such as incidence of disease recurrence or incidence of a second primary cancer.
What is the purpose of a Kaplan-Meier curve?
The Kaplan-Meier estimator is used to estimate the survival function. The visual representation of this function is usually called the Kaplan-Meier curve, and it shows what the probability of an event (for example, survival) is at a certain time interval.
How do you analyze survival data?
In cancer studies, most of survival analyses use the following methods:
- Kaplan-Meier plots to visualize survival curves.
- Log-rank test to compare the survival curves of two or more groups.
- Cox proportional hazards regression to describe the effect of variables on survival.
What is the fine gray model?
The Fine-Gray subdistribution hazard model has become the default method to estimate the incidence of outcomes over time in the presence of competing risks. This model is attractive because it directly relates covariates to the cumulative incidence function (CIF) of the event of interest.
What is Aalen Johansen estimator?
The Aalen-Johansen estimator is a multi-state (matrix) version of the Kaplan–Meier estimator for the hazard of a survival process. The estimator can be used to estimate the transition probability matrix of a Markov process with a finite number of states.
What is the fine-gray competing risk model?
Purpose: Compared with the Kaplan-Meier and Cox model, the Fine-Gray competing risk model was developed to take competing risks into account, which provides a better estimation for the risk of the main outcome of interest when one or more competing risks are presented. To date, it remains underused.
What is a fine and gray Subdistribution hazard model?
The subdistribution hazard function, introduced by Fine and Gray, for a given type of event is defined as the instantaneous rate of occurrence of the given type of event in subjects who have not yet experienced an event of that type.
How do you find the cumulative incidence curve?
Cumulative incidence is calculated as the number of new events or cases of disease divided by the total number of individuals in the population at risk for a specific time interval.