Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2 3 4 |

`formula` |
formula object where LHS is failure time and RHS is linear predictors; intercept ‘1’ should always be included. |

`scope.min` |
formula object denoting final model for backward selection and starting model for forward selection. |

`etype` |
integer variable that denotes type of failure for each person. |

`...` |
variables passed to ‘crr’ function; two key variables are |

`subset` |
subset of data to be used for model selection. |

`data` |
data-frame containing all the variables. Only complete cases are used in the analysis, i.e. rows of dataframe with missing values in any of the predictors are deleted. |

`direction` |
forward or backward direction for model selection. |

`criterion` |
selection criterion; default is AIC. BIC uses log(n) as penalty, where ‘n’ is total sample size, and BICcr uses log(n*) as the penalty where n* is the number of primary events. |

`crr.object` |
logical variable indicating whether to return final ‘crr’ object. |

`trace` |
logical indicating whether to display stepwise model selection process. |

`steps` |
maximum number of steps in stepwise selection. |

Based on the existing code of stepAIC in the MASS package. Variables passed to 'crr' function include two key variables: *failcode* and *cencode*. *failcode* is an integer value that denotes primary failure, and *cencode* is an integer denoting censoring event.

`variables` |
Variables in the final model |

`coefficients` |
The estimated coefficients of the variables |

`std.errors` |
Standard errors of the estimated coefficients |

`log.lik` |
The partial log-likelihood of the model |

Ravi Varadhan & Deborah Kuk.

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*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
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 ~ x1 + x2 + x3 + x4 + x5 + as.factor(x6) + as.factor(x7)
crrstep(formula1, , fstatus, data = as.data.frame(cov1), direction = "backward", criterion = "BIC")
ans2 <- crrstep(formula1, , fstatus, data = as.data.frame(cov1), direction = "forward",
failcode=2, criterion = "AIC")
ans2
``` |

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