iBST-package: improper Bagging Subsample Survival Tree

Description

Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importanceare computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See Cyprien Mbogning and Philippe Broet (2016)<doi:10.1186/s12859-016-1090-x> for an overview about the methods implemented in this package.

Arguments

Author

Cyprien Mbogning and Philippe Broet

Maintainer: tools:::Rd_package_maintainer("iBST")

Details

Package:	iBST
Type:	Package
Version:	1.2
Date:	2023-01-12
License:	GPL(>=2.0)

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Duhaze Julianne et al. (2020). A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort. Frontiers in Immunology, 11.

Examples

Run this code

if (FALSE) {
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 ## fit an improper survival tree
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
 ## fit an improper Bagging survival tree with the adjusted Logrank criterion
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "LR", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))
 
 ## fit an improper Bagging survival tree with the pseudo R2 criterion
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))

 ## Plot the variable importance scores
 par(mfrow=c(1,3))
barplot(burn.BagEssai1$IIS, 
   main = 'IIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'lightblue')
   
barplot(burn.BagEssai1$DIIS, 
   main = 'DIIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'grey') 
   
barplot(burn.BagEssai1$DEPTH, 
   main = 'MinDepth', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'purple')


 ## evaluation of the Bagging predictors 
pred0 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai0, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 
 
 pred1 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai1, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 }