Computation of confidence intervals for the AUC based on Bootstrap Percentile.
auc_ci_boot(marker, outcome, status, observed.time, left, right, time,
data_type, meth, grid, probs, ci.cl, ci.nboots, parallel,
ncpus, all)List with two components:
lower edge of the confidence interval.
upper edge of the confidence interval.
vector with the biomarker values.
vector with the condition of the subjects as positive, negative or unknown at the considered time time.
response vector.
vector with the observed times for each subject.
vector with the lower edges of the observed intervals.
vector with the upper edges of the observed intervals.
point of time at which the sMS ROC curve estimator will be computed.
scenario handled.
method for approximating the predictive model \(P(D|X=x)\).
grid size.
vector containing the probabilities estimated through the predictive model.
confidence level at which the confidence intervals will be computed.
number of bootstrap samples.
indicates whether parallel computing will be performed or not.
number of CPUs to use if parallel computing is performed.
indicates whether the probabilities from the predictive model will be considered for all individuals, or only for those whose outcome value (condition) is unknown.