crprep {mstate} | 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