| finegray {survival} | R Documentation |
Create data for a Fine-Gray model
Description
The Fine-Gray model can be fit by first creating a special data set, and then fitting a weighted Cox model to the result. This routine creates the data set.
Usage
finegray(formula, data, weights, subset, na.action= na.pass, etype,
prefix="fg", count, id, timefix=TRUE)
Arguments
formula |
a standard model formula, with survival on the left and covariates on the right. |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. |
weights |
optional vector of observation weights |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options. |
etype |
the event type for which a data set will be generated. The default is to use whichever is listed first in the multi-state survival object. |
prefix |
the routine will add 4 variables to the data set: a start
and end time for each interval, status, and a weight for the
interval. The default names of these are "fgstart", "fgstop", "fgstatus",
and "fgwt"; the |
count |
a variable name in the output data set for an optional variable that will contain the the replication count for each row of the input data. If a row is expanded into multiple lines it will contain 1, 2, etc. |
id |
optional, the variable name in the data set which identifies subjects. |
timefix |
process times through the |
Details
The function expects a multi-state survival expression or variable as
the left hand side of the formula, e.g. Surv(atime, astat)
where astat is a factor whose first level represents censoring
and remaining levels are states. The output data set will contain simple
survival data (status = 0 or 1) for a single endpoint of interest.
For exposition
call this endpoint A and lump all others as endpoint B.
In the output data set subjects who experience endpoint B become
censored observations
whose times are artificially extended to the right, with a
decreasing case weight from interval to interval.
The output data set will normally contain many more rows than the
input.
The algorithm allows for delayed entry, and only a limited form of
time-dependent covariates. That is, when subjects with endpoint B are
extended, those future covariate values stay constant; so there is an
implicit assumption that no more changes would have occurred if
the event had not intervened and follow-up had been longer.
For predictable time-dependent covariates the final data set could be
further processed to fix this, but this is not included in the
function. Geskus for example considers an example with different
calendar epochs, corresponding to a change in standard medical
practice for the disese, as a covariate.
dependent covariates.
If there are time dependent covariates or delayed entry, e.g.., the input data
set had Surv(entry, exit, stat) as the left hand side, then
an id statement is required. The program does data checks
in this case, and needs to know which rows belong to each subject.
The output data set will often have gaps. Say that there were events
at time 50 and 100 (and none between) and censoring at 60, 70, and 80.
Formally, a non event subjects at risk from 50 to 100 will have
different weights in each of
the 3 intervals 50-60, 60-70, and 80-100, but because the middle
interval does not span any event times the subsequent Cox model will
never use that row. The finegray output omits such rows.
See the competing risks vignette for more details.
Value
a data frame
Author(s)
Terry Therneau
References
Fine JP and Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.
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.
See Also
Examples
# Treat time to death and plasma cell malignancy as competing risks
etime <- with(mgus2, ifelse(pstat==0, futime, ptime))
event <- with(mgus2, ifelse(pstat==0, 2*death, 1))
event <- factor(event, 0:2, labels=c("censor", "pcm", "death"))
# FG model for PCM
pdata <- finegray(Surv(etime, event) ~ ., data=mgus2)
fgfit <- coxph(Surv(fgstart, fgstop, fgstatus) ~ age + sex,
weight=fgwt, data=pdata)
# Compute the weights separately by sex
adata <- finegray(Surv(etime, event) ~ . + strata(sex),
data=mgus2, na.action=na.pass)