plot.predxhaz {xhaz}

plots of excess hazard and net Survival from an predxhaz object

Description

Function to plot excess hazard or net survival

Usage

## S3 method for class 'predxhaz'
plot(x, what = "survival", ...)

Arguments

x

An object of class predxhaz

what

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

...

additional arguments affecting the plot function

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.

Author(s)

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

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)