popEpi (version 0.3.1)

rpcurve: Marginal piecewise parametric relative survival curve

Description

Fit a marginal relative survival curve based on a relpois fit

Usage

rpcurve(object = NULL)

Arguments

object

a relpois object

Details

popEpi version 0.2.1 supported confidence intervals but due to lack of testing this is disabled until the intervals are subjected to more rigorous testing.

Currently only 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 factor 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.

Examples

## Not run: 
# ## use the simulated rectal cancer cohort
# sr <- copy(sire)
# ab <- c(0,45,55,65,70,Inf)
# sr$agegr <- cut(sr$dg_age, breaks = ab, right = FALSE)
# 
# BL <- list(fot= seq(0,10,1/12))
# pm <- data.frame(popEpi::popmort)
# x <- lexpand(sr, 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")
# ## End(Not run)