Description Usage Arguments Details Author(s) Examples

CIF logistic for propodds=1 default CIF Fine-Gray (cloglog) regression for propodds=NULL

1 2 3 4 5 6 7 8 9 10 11 12 |

`formula` |
formula with 'Event' outcome |

`data` |
data frame |

`cause` |
of interest |

`cens.code` |
code of censoring |

`cens.model` |
for stratified Cox model without covariates |

`weights` |
weights for FG score equations |

`offset` |
offsets for FG model |

`Gc` |
censoring weights for time argument, default is to calculate these with a Kaplan-Meier estimator, should then give G_c(T_i-) |

`propodds` |
1 is logistic model, NULL is fine-gray model |

`...` |
Additional arguments to lower level funtions |

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. Where

*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) = ∑ X_i^j Y_{i1}(t) w_i(t) \exp(X_i^T β)*

The iid decomposition of the beta's, however, also have a censoring term that is also is computed and added to UUiid (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 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.

Thomas Scheike

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## data with no ties
data(bmt,package="timereg")
bmt$time <- bmt$time+runif(nrow(bmt))*0.01
bmt$id <- 1:nrow(bmt)
## logistic link OR interpretation
ll=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1)
summary(ll)
plot(ll)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
pll <- predict(ll,nd)
plot(pll)
## Fine-Gray model
fg=cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL)
summary(fg)
plot(fg)
nd <- data.frame(tcell=c(1,0),platelet=0,age=0)
pfg <- predict(fg,nd)
plot(pfg)
sfg <- cifreg(Event(time,cause)~strata(tcell)+platelet+age,data=bmt,cause=1,propodds=NULL)
summary(sfg)
plot(sfg)
### predictions with CI based on iid decomposition of baseline and beta
# fg <- cifreg(Event(time,cause)~tcell+platelet+age,data=bmt,cause=1,propodds=NULL,cox.prep=TRUE)
# Biid <- iid.baseline.cifreg(Biid,time=20)
# FGprediid(Biid,bmt[1:5,])
``` |

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