View source: R/calc.LL.quantile.R
calc.LL.quantile | R Documentation |
The following function estimates the time to statistical cure using the loss of lifetime function.
calc.LL.quantile( fit, q = 1, newdata = NULL, max.time = 20, var.type = c("ci", "n"), exp.fun = NULL, rmap = NULL, ratetable = cuRe::survexp.dk, tau = 100, type = "ll", scale = ayear )
fit |
Fitted model to do predictions from. Possible classes are
|
q |
Threshold to estimate statistical cure according to. |
newdata |
Data frame from which to compute predictions. If empty, predictions are made on the the data which the model was fitted on. |
max.time |
Upper boundary of the interval [0, |
var.type |
Character. Possible values are " |
exp.fun |
Object of class |
rmap |
List to be passed to |
ratetable |
Object of class |
tau |
Upper bound of integral (see ?calc.LL). Default is 100. |
type |
Type of life expectancy measure. Possible values are |
scale |
Numeric. Passed to the |
The estimated cure point.
##Use data cleaned version of the colon cancer data from the rstpm2 package data("colonDC") set.seed(2) colonDC <- colonDC[sample(1:nrow(colonDC), 1000), ] ##Extract general population hazards colonDC$bhaz <- general.haz(time = "FU", rmap = list(age = "agedays", sex = "sex", year= "dx"), data = colonDC, ratetable = survexp.dk) #Fit cure model and estimate cure point fit <- rstpm2::stpm2(Surv(FUyear, status) ~ 1, data = colonDC, df = 6, bhazard = colonDC$bhaz, cure = TRUE) calc.LL.quantile(fit, q = 1, rmap = list(age = agedays, sex = sex, year = dx))
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