# Stepwise regression procedure for the Fine & Gray regression model in competing risks Performs forward and backward stepwise regression for the Fine & Gray regression model in competing risks. Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of Type I events. package ## Details ||| |:--|:--| |Package:|crrstep| |Type:|Package| |Version:|2023.1.1| |Date:|2023-08.21| |License:|GPL (version 2)| |LazyLoad:|yes| The package contains a singe function `crrstep`, which implements backward and forward stepwise regression for the Fine & Gray regression model. The Fine & Gray model (Fine & Gray, 1999) estimates the hazard that corresponds to the cumulative incidence function of a certain event type. Selection criteria that are can be used are: AIC, BIC and BICcr. BICcr is a selection criteria based on the work by Volinksy and Raftery in which the penalty is k = log(n*), where n* is the total number of Type I events. ## Author(s) Ravi Varadhan & Deborah Kuk. Maintainers: Ravi Varadhan ## References Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. **Journal of the American Statistical Association**. Volinsky, C. T. and Raftery, A. E. (2000). Bayesian information criterion for censored survival models. **Biometrics**. Kuk, D. and Varadhan, R. (2013). Model selection in competing risks regression. **Statistics in Medicine**. ## Examples ```r set.seed(123) n <- 500 ftime <- rexp(n) fstatus <- sample(0:2,n,replace=TRUE) cov1 <- matrix(runif(5*n),nrow=n) x61 <- as.factor(sample(3, size=n, rep=TRUE)) x71 <- as.factor(sample(5, size=n, rep = TRUE)) cov1 <- cbind(cov1, x61, x71) dimnames(cov1)[[2]] <- c('x1','x2','x3','x4','x5', 'x6', 'x7') formula1 <- ftime ~ 1 + x1 + x2 + x3 + x4 + x5 + as.factor(x6) + as.factor(x7) crrstep(formula1, , fstatus, data = as.data.frame(cov1), direction = "backward", criterion = "BIC") crrstep(formula1, , fstatus, data = as.data.frame(cov1), direction = "backward", criterion = "AIC") ans2 <- crrstep(formula1, , fstatus, data = as.data.frame(cov1), direction = "forward", failcode=2, criterion = "AIC") ans2 ```

crrstep-package