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doubleFGR

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

Analysis of Multivariate Event Times

v1.2.8.1
GPL (>= 2)
Authors
Klaus K. Holst [aut, cre], Thomas Scheike [aut]
Initial release
2020-09-25

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