Double CIF Fine-Gray model with two causes

Estimation based on derived hazards and recursive estimating equations. fits two parametrizations 1) $$ F_1(t,X) = 1 - \exp( - \exp( X^T \beta ) \Lambda_1(t)) $$ and $$ F_2(t,X_2) = 1 - \exp( - \exp( X_2^T \beta_2 ) \Lambda_2(t)) $$ or restricted version 2) $$ F_1(t,X) = 1 - \exp( - \exp( X^T \beta ) \Lambda_1(t)) $$ and $$ F_2(t,X_2,X) = ( 1 - \exp( - \exp( X_2^T \beta_2 ) \Lambda_2(t)) ) (1 - F_1(\infty,X)) $$

Usage

doubleFGR(formula, data, offset = NULL, weights = NULL, X2 = NULL, ...)

Arguments

formula

formula with 'Event'

data

data frame

offset

offsets for cox model

weights

weights for Cox score equations

X2

specifies the regression design for second CIF model

...

Additional arguments to lower level funtions

Author

Thomas Scheike

Examples

library(mets)
res <- 0
data(bmt)
bmt$age2 <- bmt$age
newdata <- bmt[1:19,]
## if (interactive()) par(mfrow=c(5,3))

## same X1 and X2
pr2 <- doubleFGR(Event(time,cause)~age+platelet,data=bmt,restrict=res)
##if (interactive()) {
##  bplotdFG(pr2,cause=1)
##  bplotdFG(pr2,cause=2,add=TRUE)
##}
##pp21 <- predictdFG(pr2,newdata=newdata)
##pp22 <- predictdFG(pr2,newdata=newdata,cause=2)
##if (interactive()) {
##  plot(pp21)
##  plot(pp22,add=TRUE,col=2)
##}
##pp21 <- predictdFG(pr2)
##pp22 <- predictdFG(pr2,cause=2)
##if (interactive()) {
 ## plot(pp21)
 ## plot(pp22,add=TRUE,col=2)
##}

pr2 <- doubleFGR(Event(time,cause)~strata(platelet),data=bmt,restrict=res)

## different X1 and X2
pr2 <- doubleFGR(Event(time,cause)~age+platelet+age2,data=bmt,X2=3,restrict=res)

### uden X1
pr2 <- doubleFGR(Event(time,cause)~age+platelet,data=bmt,X2=1:2,restrict=res)

### without X2
pr2 <- doubleFGR(Event(time,cause)~age+platelet,data=bmt,X2=0,restrict=res)