fast_roc_calculation: Fast calculation of AUC for ROC using parallel strategy

In whirlsyu/EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Fast calculation of AUC for ROC using parallel strategy

Description

This function is used to create time-dependent ROC curve from censored survival data using the Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley and Pepe, 2000

Usage

fast_roc_calculation(test_matrix, y_surv, predict_time = 5, roc_method = "NNE")

Arguments

test_matrix	Test matrix used in the analysis. Colmuns are samples, rows are markers.
y_surv	Survival information created by Surv function in survival package.
predict_time	Time point of the ROC curve, default is 5 year.
roc_method	Method for fitting joint distribution of (marker,t), either of KM or NNE, the default method is NNE.

Value

This will retrun a numeric vector contains AUC results for each row in test_matrix.

Author(s)

Xin Yu

Examples

data(demo_survival_data)
data('demo_data',package = "EnMCB")
demo_set<-demo_data$realdata
res<-fast_roc_calculation(demo_set[1:2,],demo_survival_data)

whirlsyu/EnMCB documentation built on Jan. 25, 2023, 4:33 a.m.