curesurv (version 0.1.2)

predict.curesurv: Prediction for a curesurv cure model

Description

return predicted (excess) hazard, (net) survival, cure fraction and time to null excess hazard or time to cure.

Usage

# S3 method for curesurv
predict(
  object,
  newdata = NULL,
  xmax = 10^9,
  level = 0.975,
  epsilon = 0.05,
  sign_delta = 1,
  ...
)

Value

An object of class c("pred_curesurv", "data.frame"). This object is a list containing the following components:

time

time in the input new data

ex_haz

predicted excess hazard at the time provided in the new data

netsurv

predicted net survival at the time provided in the new data

pt_cure

probability to be cured

tau

time to null in model TNEH when object corresponds to the results from Boussari model or its extension.

netsurv_tau

pi or net survival at time tau when object corresponds to the results from Boussari model or its extension.

time_to_cure_ttc

time to cure (TTC)

Arguments

object

Output from curesurv function

newdata

the new data to be specified for predictions; If else, predictions are made using the data provided during the estimation step in order to obtain the output from curesurv function.

xmax

maximum time at which Time-to-Cure is evaluated numerically.

level

\(1-\frac{\alpha}{2}\)-order quantile of a normal distribution for the confidence intervals

epsilon

value fixed by user to estimate the TTC \(Pi(t) \ge 1-\epsilon\). By default epsilon = 0.05.

sign_delta

sign of effect of delta on covariates acting on survival function, positive by default "sign_delta = 1" and alternative is "sign_delta = -1"

...

additional parameters

Author

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

References

Boussari O, Bordes L, Romain G, Colonna M, Bossard N, Remontet L, Jooste V. Modeling excess hazard with time-to-cure as a parameter. Biometrics. 2021 Dec;77(4):1289-1302. doi: 10.1111/biom.13361. Epub 2020 Sep 12. PMID: 32869288. (pubmed)

Boussari O, Romain G, Remontet L, Bossard N, Mounier M, Bouvier AM, Binquet C, Colonna M, Jooste V. A new approach to estimate time-to-cure from cancer registries data. Cancer Epidemiol. 2018 Apr;53:72-80. doi: 10.1016/j.canep.2018.01.013. Epub 2018 Feb 4. PMID: 29414635. (pubmed)

Phillips N, Coldman A, McBride ML. Estimating cancer prevalence using mixture models for cancer survival. Stat Med. 2002 May 15;21(9):1257-70. doi: 10.1002/sim.1101. PMID: 12111877. (pubmed)

De Angelis R, Capocaccia R, Hakulinen T, Soderman B, Verdecchia A. Mixture models for cancer survival analysis: application to population-based data with covariates. Stat Med. 1999 Feb 28;18(4):441-54. doi: 10.1002/(sici)1097-0258(19990228)18:4<441::aid-sim23>3.0.co;2-m. PMID: 10070685. (pubmed)

See Also

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

Examples

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

fit_m2_ml <- curesurv(Surv(time_obs, event) ~ age_cr|age_cr,
                   pophaz = "ehazard",
                   cumpophaz = "cumehazard",
                   model = "mixture",
                   data = pancreas_data,
                   method_opt = "L-BFGS-B")

 fit_m2_ml

 newdata <- pancreas_data[2,]

 predict(object = fit_m2_ml, newdata = newdata)

## Non mixture cure model
### TNEH model

#### Additive parametrization

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

 predict(object = fit_m1_ad_tneh, newdata = testiscancer[3:6,])

 #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)
 head(pred_agemax)