Description Usage Arguments Details Value References See Also Examples

Computes pseudo-observations for modeling competing risks based on the cumulative incidence function.

1 |

`time` |
the follow up time. |

`event` |
the cause indicator, use 0 as censoring code and integers to name the other causes. |

`tmax` |
a vector of time points at which the pseudo-observations are to be computed. If missing, the pseudo-observations are reported at each event time. |

The function calculates the pseudo-observations for the cumulative incidence function for each individual and each risk at all the required time points.
The pseudo-observations can be used for fitting a regression model with a generalized estimating equation. No missing values in either `time`

or `event`

vector are allowed.

Please note that the output of the function has changed and the usage is thus no longer the same as in the reference paper - the new usage is described in the example below.
Similar (faster) version of the function is available in the R-package prodlim (`jackknife`

).

A list containing the following objects:

`time` |
The ordered time points at which the pseudo-observations are evaluated. |

`cause` |
The ordered codes for different causes. |

`pseudo` |
A list of matrices - a matrix for each of the causes, ordered by codes. Each row of a matrix belongs to one individual (ordered as in the original data set), each column presents a time point (ordered in time). |

Klein J.P., Gerster M., Andersen P.K., Tarima S., POHAR PERME, M.: "SAS and R Functions to Compute Pseudo-values for Censored Data Regression." *Comput. methods programs biomed.*, 2008, 89 (3): 289-300

`pseudoyl`

,
`pseudomean`

,
`pseudosurv`

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 | ```
library(KMsurv)
data(bmt)
#calculate the pseudo-observations
cutoffs <- c(50,105,170,280,530)
bmt$icr <- bmt$d1 + bmt$d3
pseudo <- pseudoci(time=bmt$t2,event=bmt$icr,tmax=cutoffs)
#rearrange the data into a long data set, use only pseudo-observations for relapse (icr=2)
b <- NULL
for(it in 1:length(pseudo$time)){
b <- rbind(b,cbind(bmt,pseudo = pseudo$pseudo[[2]][,it],
tpseudo = pseudo$time[it],id=1:nrow(bmt)))
}
b <- b[order(b$id),]
# fit the model
library(geepack)
fit <- geese(pseudo ~ as.factor(tpseudo) + as.factor(group) + as.factor(z8) +
z1 - 1, data =b, id=id, jack = TRUE, scale.fix=TRUE, family=gaussian,
mean.link = "cloglog", corstr="independence")
#The results using the AJ variance estimate
cbind(mean = round(fit$beta,4), SD = round(sqrt(diag(fit$vbeta.ajs)),4),
Z = round(fit$beta/sqrt(diag(fit$vbeta.ajs)),4),
PVal = round(2-2*pnorm(abs(fit$beta/sqrt(diag(fit$vbeta.ajs)))),4))
``` |

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