curesurv (version 0.1.2)

plot.predCuresurv: plot method for curesurv prediction objects

Description

Produces figures of (excess) hazard, (net) survival and probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t.

Usage

# S3 method for predCuresurv
plot(
  x,
  fun = "all",
  conf.int = FALSE,
  conf.type = c("log", "log-log", "plain"),
  legend.out = TRUE,
  xlab = "Time since diagnosis",
  ylab.haz = "excess hazard",
  ylab.surv = "net survival",
  ylab.ptcure = "P(t)",
  ylab.cumhaz = "cumulative excess hazard",
  ylab.logcumhaz = "logarithm of cumulative excess hazard",
  col.haz = "black",
  col.surv = "black",
  col.ptcure = "black",
  col.cumhaz = "black",
  col.logcumhaz = "black",
  col.tau = "red",
  col.ttc = "green4",
  col.p95 = "black",
  col.pi = "blue",
  lty.surv = 1,
  lty.haz = 1,
  lty.ptcure = 1,
  lty.cumhaz = 1,
  lty.logcumhaz = 1,
  lty.pi = 2,
  lty.tau = 2,
  lty.ttc = 3,
  lty.p95 = 4,
  lty.ic = 5,
  lwd.main = 1,
  lwd.sub = 1,
  lwd.ic = 1,
  ...
)

Value

No value is returned.

Arguments

x

result of the predCuresurv function

fun

in "haz" or "surv" or "pt_cure", "cumhaz", "logcumhaz", the plot produced is that of (excess) hazard, or that of (net) survival, or that of the probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t is provided, or that of cumulative hazard or that of the logarithm of the cumulative hazard; if fun = "all", the plots of the three first indicators are produced.

conf.int

an argument expected to be TRUE if the confidence intervals of the related-indicator specified by the argument "fun" are needed. The default option is FALSE. Confidence intervals are not available for fun="cumhaz" and fun="logcumhaz"

conf.type

One of "plain", "log", "log-log". The first option causes the standard intervals curve +- k *se(curve), where k is determined from conf.int. The log option calculates intervals based on log(curve). The log-log option bases the intervals on the log(-log(curve)).

legend.out

an argument deciding the place of the legend if fun="all". The default value is TRUE and forces most of the legend on the empty bottom-right plot slot. If value is FALSE, the legend will be printed entirely in each subplot.

xlab

label for the x-axis of the plot.

ylab.haz

optional label for the y-axis of the plot of excess hazard

ylab.surv

optional label for the y-axis of the plot of net survival

ylab.ptcure

optional label for the y-axis of the plot of the probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t

ylab.cumhaz

optional label for the y-axis of the plot of cumulative excess hazard

ylab.logcumhaz

optional label for the y-axis of the plot of logarithm of cumulative excess hazard

col.haz

optional argument to specify the color of curve of the excess hazard

col.surv

optional argument to specify the color of curve of the net survival

col.ptcure

optional argument to specify the color of curve of probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t.

col.cumhaz

optional argument to specify the color of curve of cumulative excess hazard

col.logcumhaz

optional argument to specify the color of curve of the logarithm of cumulative excess hazard

col.tau

optional argument to specify the color of curve of time-to-null excess hazard

col.ttc

optional argument to specify the color of curve of time-to-cure

col.p95

optional argument to specify the color for the line highlighting \(\epsilon\) when \(P(t) \ge 1-\epsilon\)

col.pi

optional argument to specify the color of cure proportion

lty.surv

stands for line types for net survival

lty.haz

stands for line types for excess hazard

lty.ptcure

stands for line types for probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t.

lty.cumhaz

stands for line types for cumulative excess hazard

lty.logcumhaz

stands for line types for logarithm cumulative excess hazard

lty.pi

stands for line types for cure proportion

lty.tau

stands for line types for time-to-null excess hazard

lty.ttc

stands for line types for time-to-cure

lty.p95

stands for line types for the line highlighting \(\epsilon\) when \(P(t) \ge 1-\epsilon\)

lty.ic

stands for line types for confidence intervals

lwd.main

line width for the main line (haz, surv, pt_cure, cumhaz, logcumhaz)

lwd.sub

line width for the additionnal lines (ttc, p95, tau...)

lwd.ic

line width for the confidence intervals lines

...

additional options as in the classical plot method.

ylab

optional label for the y-axis of the plot. Depending to the curve of interest (hazard, survival, probability of being cured at a given time t, or all),the argument must be named ylab.haz, ylab.surv, ylab.ptcure. If missing some default labels are provided depending on the curve of interest. This name can be found in the data.frame from the result of the predict.curesurv function.

Author

Juste Goungounga, Judith Breaud, Olayide Boussari, Laura Botta, Valerie Jooste

See Also

predict.curesurv(), print.curesurv(), curesurv(), browseVignettes("curesurv")

Examples

# \donttest{

library("curesurv")
library("survival")

 testiscancer$age_crmin <- (testiscancer$age- min(testiscancer$age)) /
              sd(testiscancer$age)

fit_m1_ad_tneh <- curesurv(Surv(time_obs, event) ~ z_tau(age_crmin) +
                          z_alpha(age_crmin),
                          pophaz = "ehazard",
                          cumpophaz = "cumehazard",
                          model = "nmixture", dist = "tneh",
                          link_tau = "linear",
                          data = testiscancer,
                          method_opt = "L-BFGS-B")

 fit_m1_ad_tneh


#'  #mean of age
 newdata1 <- with(testiscancer,
 expand.grid(event = 0, age_crmin = mean(age_crmin), time_obs  = seq(0.001,10,0.1)))

 pred_agemean <- predict(object = fit_m1_ad_tneh, newdata = newdata1)


 #max of age
 newdata2 <- with(testiscancer,
 expand.grid(event = 0,
 age_crmin = max(age_crmin),
  time_obs  = seq(0.001,10,0.1)))

 pred_agemax <- predict(object = fit_m1_ad_tneh, newdata = newdata2)

   # predictions at time 2 years and  of age

   newdata3 <- with(testiscancer,
      expand.grid(event = 0,
      age_crmin = seq(min(testiscancer$age_crmin),max(testiscancer$age_crmin), 0.1),
      time_obs  = 2))

   pred_age_val <- predict(object = fit_m1_ad_tneh, newdata = newdata3)

 #plot of 3 indicators for mean age

 plot(pred_agemean, fun="all")


 #plot of net survival for mean and maximum age (comparison)

oldpar <- par(no.readonly = TRUE)

par(mfrow = c(2, 2),
    cex = 1.0)
plot(pred_agemax$time,
    pred_agemax$ex_haz,
    type = "l",
    lty = 1,
    lwd = 2,
    xlab = "Time since diagnosis",
    ylab = "excess hazard")
lines(pred_agemean$time,
     pred_agemean$ex_haz,
     type = "l",
     lty = 2,
     lwd = 2)

legend("topright",
      horiz = FALSE,
      legend = c("hE(t) age.max = 79.9", "hE(t) age.mean = 50.8"),
      col = c("black", "black"),
      lty = c(1, 2, 1, 1, 2, 2))
grid()

plot(pred_agemax$time,
    pred_agemax$netsurv,
    type = "l",
    lty = 1,
    lwd = 2,
    ylim = c(0, 1),
    xlab = "Time since diagnosis",
    ylab = "net survival")
lines(pred_agemean$time,
     pred_agemean$netsurv,
     type = "l",
     lty = 2,
     lwd = 2)
legend("bottomleft",
       horiz = FALSE,
       legend = c("Sn(t) age.max = 79.9", "Sn(t) age.mean = 50.8"),
       col = c("black", "black"),
      lty = c(1, 2, 1, 1, 2, 2))
grid()

plot(pred_agemax$time,
    pred_agemax$pt_cure,
    type = "l",
    lty = 1,
    lwd = 2,
    ylim = c(0, 1), xlim = c(0,30),
    xlab = "Time since diagnosis",
    ylab = "probability of being cured P(t)")

lines(pred_agemean$time,
     pred_agemean$pt_cure,
     type = "l",
     lty = 2,
     lwd = 2)


abline(v = pred_agemean$tau[1],
      lty = 2,
      lwd = 2,
      col = "blue")
abline(v = pred_agemean$TTC[1],
       lty = 2,
       lwd = 2,
       col = "red")
abline(v = pred_agemax$tau[1],
       lty = 1,
       lwd = 2,
       col = "blue")
abline(v = pred_agemax$TTC[1],
       lty = 1,
       lwd = 2,
      col = "red")
grid()

legend("bottomright",
       horiz = FALSE,
       legend = c("P(t) age.max = 79.9",
                 "P(t) age.mean = 50.8",
                 "TNEH age.max = 79.9",
                 "TTC age.max = 79.9",
                 "TNEH age.mean = 50.8",
                 "TTC age.mean = 50.8"),
      col = c("black", "black", "blue", "red", "blue", "red"),
      lty = c(1, 2, 1, 1, 2, 2))


 val_age <- seq(min(testiscancer$age_crmin),
                max(testiscancer$age_crmin), 0.1) * sd(testiscancer$age) +
                min(testiscancer$age)


 pred_age_val <- predict(object = fit_m1_ad_tneh, newdata = newdata3)


par(mfrow=c(2,2))
 plot(val_age,
     pred_age_val$ex_haz, type = "l",
     lty=1, lwd=2,
     xlab = "age",
     ylab = "excess hazard")
grid()

 plot(val_age,
     pred_age_val$netsurv, type = "l", lty=1,
     lwd=2, xlab = "age", ylab = "net survival")
     grid()

 plot(val_age,
     pred_age_val$pt_cure, type = "l", lty=1, lwd=2,
     xlab = "age",
     ylab = "P(t)")
     grid()
par(oldpar)
 # }