ipcw_auc: IPC Weighted AUC Loss Function

In pablogonzalezginestet/EnsBagg: Stacked IPCW Bagging

IPC Weighted AUC Loss Function

Description

Compute time-dependent IPCW AUC to account for censoring and competing risks.

Usage

ipcw_auc(T, delta, marker, cause, wts, tao)

Arguments

T	vector of (censored) event-times
delta	vector of event indicators at the corresponding value of the vector T. Censored observations must be denoted by the value 0.
marker	vector of the marker values for which we want to compute the time-dependent ROC curve. the function assumes that larger values of the marker are associated with higher risks of events
cause	value of the event indicator (the non-censored observation) that represents the event of interest for which we aim to compute the time-dependent ROC curve.
wts	IPC weights
tao	evaluation time point of interest

Value

value of the AUC at tao

pablogonzalezginestet/EnsBagg documentation built on Aug. 25, 2023, 3:22 a.m.