# plot method for curesurv prediction objects 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. ```r ## S3 method for class '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, ... ) ``` ## 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. ## Returns No value is returned. ## Examples ```r 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) ``` ## See Also `predict.curesurv()`, `print.curesurv()`, `curesurv()`, `browseVignettes("curesurv")` ## Author(s) Juste Goungounga, Judith Breaud, Olayide Boussari, Laura Botta, Valerie Jooste

plot.predCuresurv function