crprep.default | R Documentation |

This function converts a dataset that is in short format (one subject per line) into a counting process format with time-varying weights that correct for right censored and left truncated data. With this data set, analyses based on the subdistribution hazard can be performed.

```
## Default S3 method:
crprep(
Tstop,
status,
data,
trans = 1,
cens = 0,
Tstart = 0,
id,
strata,
keep,
shorten = TRUE,
rm.na = TRUE,
origin = 0,
prec.factor = 1000,
...
)
```

`Tstop` |
Either 1) a vector containing the time at which the follow-up
is ended, or 2) a character string indicating the column name in |

`status` |
Either 1) a vector describing status at end of follow-up,
having the same length as |

`data` |
Data frame in which to interpret |

`trans` |
Values of |

`cens` |
Value that denotes censoring in |

`Tstart` |
Either 1) a vector containing the time at which the follow-up
is started, having the same length as |

`id` |
Either 1) a vector, having the same length as |

`strata` |
Either 1) a vector of the same length as |

`keep` |
Either 1) a data frame or matrix or a numeric or factor vector
containing covariate(s) that need to be retained in the output dataset.
Number of rows/length should correspond with |

`shorten` |
Logical. If true, number of rows in output is reduced by collapsing rows within a subject in which weights do not change. |

`rm.na` |
Logical. If true, rows for which |

`origin` |
Substract origin time units from all Tstop and Tstart times. |

`prec.factor` |
Factor by which to multiply the machine's precision. Censoring and truncation times are shifted by prec.factor*precision if event times and censoring/truncation times are equal. |

`...` |
Further arguments to be passed to or from other methods. They are ignored in this function. |

For each event type as specified via `trans`

, individuals with a
competing event remain in the risk set with weights that are determined by
the product-limit forms of the time-to-censoring and time-to-entry
estimates. Typically, their weights change over follow-up, and therefore
such individuals are split into several rows. Censoring weights are always
computed. Truncation weights are computed only if `Tstart`

is
specified.

If several event types are specified at once, regression analyses using the
stacked format data set can be performed (see Putter et al. 2007 and Chapter
4 in Geskus 2016). The data set can also be used for a regression on the
cause-specific hazard by restricting to the subset `subset=count==0`

.

Missing values are allowed in `Tstop`

, `status`

, `Tstart`

,
`strata`

and `keep`

. Rows for which `Tstart`

or `Tstart`

is missing are deleted.

There are two ways to supply the data. If given "by value" (option 1), the
actual data vectors are used. If given "by name" (option 2), the column
names are specified, which are read from the data set in `data`

. In
general, the second option is preferred.

If data are given by value, the following holds for the naming of the
columns in the output data set. If `keep`

, `strata`

or `id`

is a vector from a (sub)-list, e.g. obj$name2$name1, then the column name is
based on the most inner part (i.e.\ "name1"). If it is a vector of the form
obj[,"name1"], then the column is named "name1". For all other vector
specifications, the name is copied as is. If `keep`

is a data.frame or
a named matrix, the same names are used for the covariate columns in the
output data set. If keep is a matrix without names, then the covariate
columns are given the names "V1" until "Vk".

The current function does not allow to create a weighted data set in which the censoring and/or truncation mechanisms depend on covariates via a regression model.

A data frame in long (counting process) format containing the covariates (replicated per subject). The following column names are used:

`Tstart` |
start dates of dataset |

`Tstop` |
stop dates of dataset |

`status` |
status of the subject at the end of that row |

`weight.cens` |
weights due to censoring mechanism |

`weight.trunc` |
weights due to truncation mechanism (if present) |

`count` |
row number within subject and event type under consideration |

`failcode` |
event type under consideration |

The first column is the subject identifier. If the argument "id" is missing,
it has values 1:n and is named "id". Otherwise the information is taken from
the `id`

argument.

Variables as specified in `strata`

and/or `keep`

are included as
well (see Details).

Ronald Geskus

Geskus RB (2011). Cause-Specific Cumulative Incidence Estimation
and the Fine and Gray Model Under Both Left Truncation and Right Censoring.
*Biometrics* **67**, 39–49.

Geskus, Ronald B. (2016). *Data Analysis with Competing Risks and
Intermediate States.* CRC Press, Boca Raton.

Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing
risks and multi-state models. *Statistics in Medicine* **26**,
2389–2430.

```
data(aidssi)
aidssi.w <- crprep("time", "cause", data=aidssi, trans=c("AIDS","SI"),
cens="event-free", id="patnr", keep="ccr5")
# calculate cause-specific cumulative incidence, no truncation,
# compare with Cuminc (also from mstate)
ci <- Cuminc(aidssi$time, aidssi$status)
sf <- survfit(Surv(Tstart,Tstop,status=="AIDS")~1, data=aidssi.w,
weight=weight.cens, subset=failcode=="AIDS")
plot(sf, fun="event", mark.time=FALSE)
lines(CI.1~time,data=ci,type="s",col="red")
sf <- survfit(Surv(Tstart,Tstop,status=="SI")~1, data=aidssi.w,
weight=weight.cens, subset=failcode=="SI")
plot(sf, fun="event", mark.time=FALSE)
lines(CI.2~time,data=ci,type="s",col="red")
# Fine and Gray regression for cause 1
cw <- coxph(Surv(Tstart,Tstop,status=="AIDS")~ccr5, data=aidssi.w,
weight=weight.cens, subset=failcode=="AIDS")
cw
# This can be checked with the results of crr (cmprsk)
# crr(ftime=aidssi$time, fstatus=aidssi$status, cov1=as.numeric(aidssi$ccr5))
# Gray's log-rank test
aidssi.wCCR <- crprep("time", "cause", data=aidssi, trans=c("AIDS","SI"),
cens="event-free", id="patnr", strata="ccr5")
test.AIDS <- coxph(Surv(Tstart,Tstop,status=="AIDS")~ccr5, data=aidssi.wCCR,
weights=weight.cens, subset=failcode=="AIDS")
test.SI <- coxph(Surv(Tstart,Tstop,status=="SI")~ccr5, data=aidssi.wCCR,
weights=weight.cens, subset=failcode=="SI")
## score test statistic and p-value
c(test.AIDS$score, 1-pchisq(test.AIDS$score,1)) # AIDS
c(test.SI$score, 1-pchisq(test.SI$score,1)) # SI
# This can be compared with the results of cuminc (cmprsk)
# with(aidssi, cuminc(time, status, group=ccr5)$Tests)
# Note: results are not exactly the same
```

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