popEpi (version 0.4.12)

rpcurve: Marginal piecewise parametric relative survival curve

Description

Fit a marginal relative survival curve based on a relpois fit

Usage

rpcurve(object)

Value

A `data.table` of relative survival curves.

Arguments

object

a relpois object

Author

Joonas Miettinen

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.

See Also

Other relpois functions: RPL, relpois(), relpois_ag()

Examples

# \donttest{
## 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")
# }