simulateTwoCauseFineGrayModel: Simulate data from the Fine-Gray Model
In erickawaguchi/crrBAR: Broken Adaptive Ridge for Competing Risks Regression

Description Usage Arguments Details Value References Examples

Simulate data from the model proposed in Fine and Gray (1999) for two causes. Cause 1 is assumed to be of primary importance.

1 2	simulateTwoCauseFineGrayModel(nobs, beta1, beta2, X = NULL, u.min = 0, u.max, p = 0.5, returnX = FALSE)

`nobs`	Integer: Number of observations in simulated dataset.
`beta1`	A vector of effect sizes for cause 1 of length ncovs
`beta2`	A vector of effect sizes for cause 2 of length ncovs
`X`	A matrix of fixed covariates (nobs x ncovs). If `X` is NULL (default) then `X` will be simulated from MVN(O, I) with n = nobs and p = length(beta1).
`u.min`	Numeric: controls lower bound of censoring distribution where C ~ U(u.min, u.max)
`u.max`	Numeric: controls upper bound of censoring distribution where C ~ U(u.min, u.max)
`p`	Numeric: value between 0 and 1 which controls the mixture probability.
`returnX`	Logical: Whether to return `X` or not. Default is TRUE. Recommended if `X` is NULL.

The function simulates data according to the setup by Fine and Gray (1999). See their paper for more information.

Returns a list with ftime, fstatus, X.

Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.

set.seed(10)
nobs <- 500
beta1 <- c(0.5, 0.4, 0, 0, 0.35, 0, 0, 0.7)
beta2 <- -beta1
X <- matrix(rnorm(nobs * 8), nrow = nobs)
dat <- simulateTwoCauseFineGrayModel(nobs, beta1, beta2, X, u.min = 0, u.max = 1, p = 0.5)