How to do a competing risk regression using R?
Regression modeling of competing risk using R: an in depth guide for clinicians We describe how to conduct a regression analysis for competing risks data.
Can a hierarchical structure be accommodated with frailty?
Hierarchical structure in your data can be accommodated with cluster or frailty (random effects) terms. Competing risks regression may be useful if your outcome is in competition with another, such as all-cause death, but is currently limited in its ability to accommodate hierarchical structures.
Can a fine and gray model be used for a competing risk analysis?
The Fine and Gray model can also be extended to allow for time-dependent covariates. Today, analysis of competing data using either non-parametric or parametric method is available in the major statistical packages including R , STATA and SAS.
How to calculate semiparametric proportional hazards using R?
The use of an add-on package for the R statistical software is described, which allows for the estimation of the semiparametric proportional hazards model for the subdistribution of a competing risk analysis as proposed by Fine and Gray. J Am Stat Assoc 1999; 94: 496-509. MeSH terms
In our earlier publication, 4 we presented a description of the univariate competing risk analysis performed using the R statistical software. 5 Furthermore, we discussed how to perform significance testing when different groups are involved. Here, we show how to perform a multivariable regression analysis in the presence of competing risks data.
What do you need to know about competing risk analysis?
1 Overview. 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. 2 Description. What is “competing event” and “competing risk”? 3 Readings. J.
How to analyze survival data with competing risk?
During the last two decades, many authors have proposed different methods to analyze survival data in the presence of competing risk (see below for a brief review), but applications from clinicians, including those who are well trained in medical statistics, are still not currently performed.