doubleFGR: Double CIF Fine-Gray model with two causes

In mets: Analysis of Multivariate Event Times

Double CIF Fine-Gray model with two causes

Description

Estimation based on derived hazards and recursive estimating equations. fits two parametrizations 1)

F_1(t,X) = 1 - \exp( - \exp( X^T β ) Λ_1(t))

and

F_2(t,X_2) = 1 - \exp( - \exp( X_2^T β_2 ) Λ_2(t))

or restricted version 2)

F_1(t,X) = 1 - \exp( - \exp( X^T β ) Λ_1(t))

and

F_2(t,X_2,X) = ( 1 - \exp( - \exp( X_2^T β_2 ) Λ_2(t)) ) (1 - F_1(∞,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(s)

Thomas Scheike

Examples

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)
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)
}

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

### uden X1
pr2 <- doubleFGR(Event(time,cause)~age+platelet,data=bmt,X2=1:2,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)
p22 <- predictdFG(pr2,cause=2)
if (interactive()) {
  plot(pp21)
  plot(pp22,add=TRUE,col=2)
}

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

mets documentation built on Jan. 17, 2023, 5:12 p.m.