finegray: Create data for a Fine-Gray model

In survival: Survival Analysis

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 prefix argument determines the initial portion of the new names.
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 aeqSurv function to eliminate potential roundoff issues.

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

coxph, aeqSurv

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)

survival documentation built on June 22, 2024, 10:49 a.m.