sMS_timerc {sMSROC}

sMS estimator for prognostic biomarkers and right censorship

Description

Wrap function for computing the sMS estimator in prognosis scenarios under right censorship.

Usage

sMS_timerc(marker, status, observed.time, outcome, time,
           meth, grid, probs, all)

Arguments

marker

vector with the biomarker values.

status

numeric response vector.

observed.time

vector with the observed times. These values may be the event times or the censoring times.

outcome

vector containing the condition of the individuals as positive, negative or censored (unknown) at the time time.

time

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

meth

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

• “E”, allocates to each individual their own condition as positive or negative. Those whose condition is unknown at time time are dismissed.

• “L”, for Linear proportional hazards regression models (see details in sMSROC).

• “S”, for Smooth models (see details in sMSROC).

grid

grid size.

probs

vector with the probabilities from the predictive model when it is manually entered.

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”).

Details

This function gets the probabilities corresponding to the predictive model (first stage of the sMS ROC curve estimator). If they were not manually entered, the functions pred.mod.emp or pred.mod.timerc are called depending on the chosen meth. Then, it calls the function computeROC to compute the weighted empirical ROC curve estimator (second stage).

Value

The returned value is a list with the following components:

SE

vector with the weighted empirical estimator of the sensitivity.

SP

vector with the weighted empirical estimator of the specificity.

u

vector containing the points between 0 and 1 at which the ROC curve estimator will be computed. Its size is determined by the grid parameter.

ROC

ROC curve approximated at each point of the vector u.

auc

area under the weighted empirical ROC curve estimator.

marker

vector with the ordered biomarker values.

outcome

vector with the condition of the individuals at time time as positive, negative or cenrored (unknown).

probs

vector with the probabilities of the predictive model corresponding to each biomarker value.

See Also

pred.mod.emp, pred.mod.binout, computeROC, sMS.timerc, sMS.timeic