auc_ci_nvar-internal {sMSROC}

Confidence intervals for the AUC (theoretical variance estimation)

Description

Computation of confidence intervals for the AUC by implementing the theoretical procedure for estimating the variance of the AUC, as described in doi:10.1515/ijb-2019-0097.

Usage

auc_ci_nvar(marker, outcome, status, observed.time, left, right, time,
                    meth, data_type, grid, probs, sd.probs, ci.cl, nboots,
                    SE, SP, auc, parallel, ncpus, all)

Arguments

marker

vector with the biomarker values.

outcome

vector with the condition of the subjects as positive, negative or unknown at the considered time time.

status

response vector.

observed.time

vector with the observed times for each subject.

left

vector with the lower edges of the observed intervals.

right

vector with the upper edges of the observed intervals.

time

point of time at which the sMS ROC curve estimator will be computed.

meth

method for approximating the predictive model P(D|X=x).

data_type

scenario handled.

grid

grid size.

probs

vector containing the probabilities estimated through the predictive model.

sd.probs

vector containing the standard deviation of the probabilities of the predictive model.

ci.cl

confidence levet at which the confidence intervals will be computed.

nboots

number of bootstrap samples.

SE

vector containing the values of the sensitivity returned from sMSROC function.

SP

vector containing the values of the specificity.

auc

value with the AUC estimate.

parallel

indicates whether parallel computing will be performed or not.

ncpus

number of CPUs to use if parallel computing is performed.

all

parameter indicating whether all probabilities given by the predictive model should be considered (value “T”) or just those corresponding to individuals whose condition as positive or negative is unknown (“F”). The default value is (“T”).

Value

List with two components:

ic.l

lower edge of the confidence interval.

ic.u

upper edge of the confidence interval.