bootstrapTimeToEvent {eventTrack} | R Documentation |
Bootstrap the predicted time when a given number of events is reached, for hybrid Exponential model
Description
Bootstrap the predicted time when a given number of events is reached, based on the hybrid Exponential model.
Usage
bootstrapTimeToEvent(time, event, interim.gates, future.units, n, M0 = 1000,
K0 = 5, alpha = 0.05, accrual, seed = 2014)
Arguments
time |
Event times. |
event |
Censoring indicator, 0 = censored, 1 = event. |
interim.gates |
Number of events for which confidence intervals should be computed. May be a vector. |
future.units |
Number of months for which predictions are to be made. |
n |
Size of the bootstrap samples to be drawn from |
M0 |
Number of bootstrap samples to be drawn. |
K0 |
Number of changepoints for the piecewise constant hazard. |
alpha |
Familywise error rate for the sequential test. |
accrual |
Vector of dates when future patients enter the study. As for the specification, assume 50 pts are to be recruited in Dec 2013 and 20pts in Jan 2014, then use |
seed |
Seed for generation of bootstrap samples. |
Value
A list containing the following objects:
pe.MLEs |
Piecewise Exponential MLE object for each bootstrap sample, output of function |
pe.tabs |
Result of sequential test for each bootstrap sample, output of function |
changepoints |
For each bootstrap sample, changepoint as resulting from sequential test. |
estS |
Estimated survival function for each bootstrap sample. |
event.dates |
Event dates for each bootstrap sample, where columns relate to the number of events in |
Author(s)
Kaspar Rufibach (maintainer)
kaspar.rufibach@roche.com
References
Rufibach, K. (2016). Event projection: quantify uncertainty and manage expectations of broader teams. Slides for talk given in Basel Biometric Section Seminar on 28th April 2016. https://baselbiometrics.github.io/home/docs/talks/20160428/1_Rufibach.pdf.
Examples
## Not run:
# --------------------------------------------------
# simulate data for illustration
# --------------------------------------------------
set.seed(2021)
n <- 600
time <- rexp(n, rate = log(2) / 20)
event <- sample(round(runif(n, 0, 1)))
accrual_after_ccod <- 1:(n - length(time)) / 30
# --------------------------------------------------
# run bootstrap, for M0 = 3 only, for illustration
# tune parameters for your own example
# --------------------------------------------------
boot1 <- bootstrapTimeToEvent(time, event,
interim.gates = c(330, 350), future.units = 50, n = length(time),
M0 = 3, K0 = 5, alpha = 0.05, accrual = accrual_after_ccod,
seed = 2014)
# median of bootstrap samples:
apply(boot1$event.dates, 2, median)
## End(Not run)