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

 FGR R Documentation

## Formula wrapper for crr from cmprsk

### Description

Formula interface for Fine-Gray regression competing risk models.

### Usage

```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. Note that FGR does not allow for delayed entry (left-truncation).

### Value

See `crr`.

### Author(s)

Thomas Alexander Gerds tag@biostat.ku.dk

### 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`

### Examples

```
library(prodlim)
library(survival)
library(cmprsk)
library(lava)
d <- prodlim::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)