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.