cifreg: CIF regression

In kkholst/mets: Analysis of Multivariate Event Times

CIF regression

Description

CIF logistic-link for propodds=1 default and CIF Fine-Gray (cloglog) regression for propodds=NULL. The FG model can also be called using the cifregFG function that has propodds=NULL.

Usage

cifreg(
  formula,
  data,
  propodds = 1,
  cause = 1,
  cens.code = 0,
  no.codes = NULL,
  ...
)

Arguments

formula	formula with 'Event' outcome
data	data frame
propodds	to fit logit link model, and propodds=NULL to fit Fine-Gray model
cause	of interest
cens.code	code of censoring
no.codes	certain event codes to be ignored when finding competing causes
...	Additional arguments to recreg

Details

For FG model:

\int (X - E ) Y_1(t) w(t) dM_1

is computed and summed over clusters and returned multiplied with inverse of second derivative as iid.naive. Here

w(t) = G(t) (I(T_i \wedge t < C_i)/G_c(T_i \wedge t))

and

E(t) = S_1(t)/S_0(t)

and

S_j(t) = \sum X_i^j Y_{i1}(t) w_i(t) \exp(X_i^T \beta)

.

The iid decomposition of the beta's, however, also have a censoring term that is also is computed and added (still scaled with inverse second derivative)

\int (X - E ) Y_1(t) w(t) dM_1 + \int q(s)/p(s) dM_c

and returned as the iid

For logistic link standard errors are slightly to small since uncertainty from recursive baseline is not considered, so for smaller data-sets it is recommended to use the prop.odds.subdist of timereg that is also more efficient due to use of different weights for the estimating equations. Alternatively, one can also bootstrap the standard errors.

Author(s)

Thomas Scheike

Examples

## data with no ties
library(mets)
data(bmt,package="mets")
bmt$time <- bmt$time+runif(nrow(bmt))*0.01
bmt$id <- 1:nrow(bmt)

## logistic link  OR interpretation
or=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)
summary(or)
par(mfrow=c(1,2))
plot(or)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
por <- predict(or,nd)
plot(por)

## Fine-Gray model
fg=cifregFG(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)
summary(fg)
plot(fg)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
pfg <- predict(fg,nd,se=1)
plot(pfg,se=1)

## not run to avoid timing issues
## gofFG(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)

sfg <- cifregFG(Event(time,cause)~strata(tcell)+platelet+age,data=bmt,cause=1)
summary(sfg)
plot(sfg)

### predictions with CI based on iid decomposition of baseline and beta
### these are used in the predict function above
fg <- cifregFG(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)
Biid <- iidBaseline(fg,time=20)
pfg1 <- FGprediid(Biid,nd)
pfg1

kkholst/mets documentation built on June 14, 2025, 9:19 a.m.