predict.curesurv {curesurv} | R Documentation |
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 class 'curesurv'
predict(
object,
newdata = NULL,
xmax = 10^9,
level = 0.975,
epsilon = 0.05,
sign_delta = 1,
...
)
Arguments
object |
Output from |
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 |
|
epsilon |
value fixed by user to estimate the TTC |
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 |
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) |
Author(s)
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)