# FGR: Formula wrapper for crr from cmprsk In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

## Description

Formula interface for Fine-Gray regression competing risk models.

## Usage

 `1` ```FGR(formula, data, cause = 1, y = TRUE, ...) ```

## Arguments

 `formula` A formula whose left hand side is a `Hist` object – see `Hist`. The right hand side specifies (a linear combination of) the covariates. See examples below. `data` A data.frame in which all the variables of `formula` can be interpreted. `cause` The failure type of interest. Defaults to `1`. `y` logical value: if `TRUE`, the response vector is returned in component `response`. `...` ...

## Details

Formula interface for the function `crr` from the `cmprsk` package.

The function `crr` allows to multiply some covariates by time before they enter the linear predictor. This can be achieved with the formula interface, however, the code becomes a little cumbersome. See the examples.

## Value

See `crr`.

## Author(s)

Thomas Alexander Gerds [email protected]

## References

Gerds, TA and Scheike, T and Andersen, PK (2011) Absolute risk regression for competing risks: interpretation, link functions and prediction Research report 11/7. Department of Biostatistics, University of Copenhagen

`riskRegression`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49``` ```library(prodlim) library(survival) library(cmprsk) library(lava) d <- SimCompRisk(100) f1 <- FGR(Hist(time,cause)~X1+X2,data=d) print(f1) ## crr allows that some covariates are multiplied by ## a function of time (see argument tf of crr) ## by FGR uses the identity matrix f2 <- FGR(Hist(time,cause)~cov2(X1)+X2,data=d) print(f2) ## same thing, but more explicit: f3 <- FGR(Hist(time,cause)~cov2(X1)+cov1(X2),data=d) print(f3) ## both variables can enter cov2: f4 <- FGR(Hist(time,cause)~cov2(X1)+cov2(X2),data=d) print(f4) ## change the function of time qFun <- function(x){x^2} noFun <- function(x){x} sqFun <- function(x){x^0.5} ## multiply X1 by time^2 and X2 by time: f5 <- FGR(Hist(time,cause)~cov2(X1,tf=qFun)+cov2(X2),data=d) print(f5) print(f5\$crrFit) ## same results as crr with(d,crr(ftime=time, fstatus=cause, cov2=d[,c("X1","X2")], tf=function(time){cbind(qFun(time),time)})) ## still same result, but more explicit f5a <- FGR(Hist(time,cause)~cov2(X1,tf=qFun)+cov2(X2,tf=noFun),data=d) f5a\$crrFit ## multiply X1 by time^2 and X2 by sqrt(time) f5b <- FGR(Hist(time,cause)~cov2(X1,tf=qFun)+cov2(X2,tf=sqFun),data=d,cause=1) ## additional arguments for crr f6<- FGR(Hist(time,cause)~X1+X2,data=d, cause=1,gtol=1e-5) f6 f6a<- FGR(Hist(time,cause)~X1+X2,data=d, cause=1,gtol=0.1) f6a ```