Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register

Pablo Gonzalez Ginestet, Ales Kotalik, David M. Vock, Julian Wolfson, Erin E. Gabriel

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
DOIs
StateAccepted/In press - 2020

Keywords

  • competing risk
  • cumulative incidence
  • ensemble learning
  • HIV treatment
  • inverse probability of censoring weighting
  • machine learning
  • stacking

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