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Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register
Author(s) -
Gonzalez Ginestet Pablo,
Kotalik Ales,
Vock David M.,
Wolfson Julian,
Gabriel Erin E.
Publication year - 2021
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12448
Subject(s) - censoring (clinical trials) , inverse probability , computer science , statistics , bootstrap aggregating , metric (unit) , mathematics , artificial intelligence , posterior probability , bayesian probability , engineering , operations management
We propose an inverse probability of censoring weighted (IPCW) bagging (bootstrap aggregation) pre‐processing that enables the application of any machine learning procedure for classification to be used to predict the cause‐specific cumulative incidence, properly accounting for right‐censored observations and competing risks. We consider the IPCW area under the time‐dependent ROC curve (IPCW‐AUC) as a performance evaluation metric. We also suggest a procedure to optimally stack predictions from any set of IPCW bagged methods. We illustrate our proposed method in the Swedish InfCareHIV register by predicting individuals for whom treatment will not maintain an undetectable viral load for at least 2 years following initial suppression. The R package stackBagg that implements our proposed method is available on Github.