predict.constant {xhaz} | R Documentation |
Predictions of excess hazard and net Survival from an constant
object
Description
Function to predict excess hazard and net survival based on an
object of class constant
. The function allows the
predictions at several time points but not exceeding the maximum time of
follow-up from the baseline model.
Usage
## S3 method for class 'constant'
predict(object, new.data = NULL, times.pts = NULL, baseline = TRUE, ...)
Arguments
object |
An object of class constant |
new.data |
new.data where is covariates |
times.pts |
time in year scale to calculate the excess hazard. The default value is NULL. In this case, time variable must be provided in the new.data |
baseline |
default is survival baseline; put |
... |
additional arguments affecting the predictions of excess hazard and net survival |
Value
An object of class predxhaz. The return of this fonction can be used to produce graphics of excess hazard or net survival, when times.pts argument is provided. This object contains:
times.pts |
the times value in year at which the excess hazard and or the net survival have been estimated |
hazard |
the excess hazard values based on the model of interest |
survival |
the net survival values based on the model of interest |
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)
See Also
xhaz
, print.bsplines
, print.constant
Examples
# load the data set in the package
library("xhaz")
library("numDeriv")
# load the data sets 'simuData
data("simuData", package = "xhaz")
#define the levels of variable sex
levels(simuData$sex) <- c("male", "female")
# Esteve et al. model
set.seed(1980)
simuData2 <- simuData[sample(nrow(simuData), size = 500), ]
fit.estv2 <- xhaz(formula = Surv(time_year, status) ~ agec + race,
data = simuData2,
ratetable = survexp.us,
interval = c(0, NA, NA, NA, NA, NA, 6),
rmap = list(age = 'age', sex = 'sex', year = 'date'),
baseline = "constant", pophaz = "classic")
predict_est <- predict(object = fit.estv2,
new.data = simuData2,
times.pts = c(seq(0, 4, 1)),
baseline = TRUE)
predict_est