xhaz (version 2.1.0)

plot.predxhaz: plots of excess hazard and net Survival from an predxhaz object

Description

Function to plot excess hazard or net survival

Usage

# S3 method for predxhaz
plot(x, what = c("survival", "hazard"), add = FALSE, step = NULL, ...)

Value

The return of this function produce graphics of excess hazard or net survival, or time-dependent effects, when times.pts argument is provided in prediction call.

Arguments

x

An object of class predxhaz or coercible

what

allow to choose between excess hazard (what="hazard") or net survival (what="survival").

add

if TRUE, add to an existing plot.

step

if TRUE and what="hazard", the excess hazard is plotted by a piecewise constant function (step function). By default, it is set to TRUE when the baseline hazard is piecewise constant.

...

additional arguments affecting the plot function

Author

Juste Goungounga, Robert Darlin Mba, Nathalie Graff\'eo and Roch Giorgi

References

Goungounga JA, Touraine C, Graff\'eo N, Giorgi R; CENSUR working survival group. Correcting for misclassification and selection effects in estimating net survival in clinical trials. BMC Med Res Methodol. 2019 May 16;19(1):104. doi: 10.1186/s12874-019-0747-3. PMID: 31096911; PMCID: PMC6524224. (PubMed)

Touraine C, Graff\'eo N, Giorgi R; CENSUR working survival group. More accurate cancer-related excess mortality through correcting background mortality for extra variables. Stat Methods Med Res. 2020 Jan;29(1):122-136. doi: 10.1177/0962280218823234. Epub 2019 Jan 23. PMID: 30674229. (PubMed)

Mba RD, Goungounga JA, Graff\'eo N, Giorgi R; CENSUR working survival group. Correcting inaccurate background mortality in excess hazard models through breakpoints. BMC Med Res Methodol. 2020 Oct 29;20(1):268. doi: 10.1186/s12874-020-01139-z. PMID: 33121436; PMCID: PMC7596976. (PubMed)

Examples

# \donttest{
data("dataCancer")
# load the data set in the package
library("survival")
library("numDeriv")
library("survexp.fr")
data("simuData", package = "xhaz") # load the data sets 'simuData'

#define the levels of variable sex

# Esteve et al. model


fit.estv1 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
                  data = simuData, ratetable = survexp.us,
                  interval = c(0, NA, NA, NA, NA, NA, max(simuData$time_year)),
                  rmap = list(age = 'age', sex = 'sex', year = 'date'),
                  baseline = "constant", pophaz = "classic")


predict_est <- predict(object = fit.estv1,
                       new.data = simuData,
                       times.pts = c(seq(0, 4, 0.1)),
                       baseline = TRUE)

plot(predict_est, what = "survival",
     xlab = "time since diagnosis (year)",
     ylab = "net survival", ylim = c(0, 1))
data("dataCancer", package = "xhaz")   # load the data set in the package

fit.phBS <- xhaz(
      formula = Surv(obs_time_year, event) ~ ageCentre + immuno_trt,
      data = dataCancer, ratetable = survexp.fr::survexp.fr,
      interval = c(0, NA, NA, max(dataCancer$obs_time_year)),
      rmap = list(age = 'age', sex = 'sexx', year = 'year_date'),
      baseline = "bsplines", pophaz  = "classic")


predict_mod1 <- predict(object = fit.phBS, new.data = dataCancer,
                        times.pts = c(seq(0, 10, 0.1)), baseline = FALSE)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(2, 1))


plot(predict_mod1, what = "survival",
     xlab = "time since diagnosis (year)",
     ylab = "net survival", ylim = c(0, 1))

plot(predict_mod1, what = "hazard",
     xlab = "time since diagnosis (year)",
     ylab = "excess hazard")

par(old.par)
# }