calc.Crude.quantile {cuRe} | R Documentation |
Compute the time to statistical cure using the conditional probability of disease-related death
Description
The following function estimates the time to statistical cure using the conditional probability of disease-related death.
Usage
calc.Crude.quantile(
fit,
q = 0.05,
newdata = NULL,
max.time = 20,
exp.fun = NULL,
var.type = c("ci", "se", "n"),
rmap,
ratetable = cuRe::survexp.dk,
tau = 100,
reverse = TRUE,
scale = ayear
)
Arguments
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, |
exp.fun |
Object of class |
var.type |
Character. Possible values are " |
rmap |
List to be passed to |
ratetable |
Object of class |
tau |
Upper bound of integral (see ?calc.Crude). Default is 100. |
reverse |
Logical passed on to |
scale |
Numeric. Passed to the |
Details
The cure point is calculated as the time point at which the conditional probability of disease-related
death reaches the threshold, q
. If q
is not reached within max.time
, no solution is reported.
Value
The estimated cure point.
Examples
##Use data cleaned version of the colon cancer data from the rstpm2 package
data("colonDC")
set.seed(2)
colonDC <- colonDC[sample(1:nrow(colonDC), 500), ]
##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)
cp <- calc.Crude.quantile(fit, q = 0.05,
rmap = list(age = agedays, sex = sex, year = dx))
#Compare the result with the trajectory of the conditional probability of disease-related death
res <- calc.Crude(fit, type = "condother", time = seq(0, 20, length.out = 100),
var.type = "n",
rmap = list(age = agedays, sex = sex, year = dx), reverse = TRUE)
plot(res)
abline(h = 0.05, v = cp$Estimate)