rpcurve {popEpi} | R Documentation |
Marginal piecewise parametric relative survival curve
Description
Fit a marginal relative survival curve based on a relpois
fit
Usage
rpcurve(object)
Arguments
object |
a |
Details
Estimates a marginal curve, i.e. the average of all possible individual curves.
Only supported when the reserved FOT
variable was used in relpois
.
Computes a curve for each unique combination of covariates (e.g. 4 sets)
and returns a weighted average curve based on the counts
of subjects for each combination (e.g. 1000, 125, 50, 25 respectively).
Fairly fast when only categorical variables have been used, otherwise
go get a cup of coffee.
If delayed entry is present in data due to period analysis limiting, the marginal curve is constructed only for those whose follow-up started in the respective period.
Value
A 'data.table' of relative survival curves.
Author(s)
Joonas Miettinen
See Also
Other relpois functions:
RPL
,
relpois()
,
relpois_ag()
Examples
## use the simulated rectal cancer cohort
data("sire", package = "popEpi")
ab <- c(0,45,55,65,70,Inf)
sire$agegr <- cut(sire$dg_age, breaks = ab, right = FALSE)
BL <- list(fot= seq(0,10,1/12))
pm <- data.frame(popEpi::popmort)
x <- lexpand(sire, breaks=BL, pophaz=pm,
birth = bi_date,
entry = dg_date, exit = ex_date,
status = status %in% 1:2)
rpm <- relpois(x, formula = lex.Xst %in% 1:2 ~ -1+ FOT + agegr,
fot.breaks=c(0,0.25,0.5,1:8,10))
pmc <- rpcurve(rpm)
## compare with non-parametric estimates
names(pm) <- c("sex", "per", "age", "haz")
x$agegr <- cut(x$dg_age, c(0,45,55,65,75,Inf), right = FALSE)
st <- survtab(fot ~ adjust(agegr), data = x, weights = "internal",
pophaz = pm)
plot(st, y = "r.e2.as")
lines(y = pmc$est, x = pmc$Tstop, col="red")