mets (version 1.3.6)

cifreg: 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

Author

Thomas Scheike

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.

Examples

## data with no ties
library(mets)
data(bmt,package="timereg")
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.cifreg(fg,time=20)
pfg1 <- FGprediid(Biid,nd)
pfg1