# finegray

0th

Percentile

##### Create data for a Fine-Gray model

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.

Keywords
survival
##### 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. In the output data set subjects who did not experience the event of interest become censored subjects whose times are artificially extended over multiple intervals, with a decreasing case weight from interval to interval. The output data set will normally contain many more rows than the input.

Time dependent covariates are allowed, but not (currently) delayed entry. If there are time dependent covariates, 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.

See the competing risks vignette for more details.

a data frame

##### 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.

coxph, aeqSurv

• finegray
##### Examples
# NOT RUN {
# 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)
# }

Documentation reproduced from package survival, version 3.1-8, License: LGPL (>= 2)

### Community examples

Looks like there are no examples yet.