Doubly robust survival trees. Academic Article uri icon

Overview

abstract

  • Estimating a patient's mortality risk is important in making treatment decisions. Survival trees are a useful tool and employ recursive partitioning to separate patients into different risk groups. Existing 'loss based' recursive partitioning procedures that would be used in the absence of censoring have previously been extended to the setting of right censored outcomes using inverse probability censoring weighted estimators of loss functions. In this paper, we propose new 'doubly robust' extensions of these loss estimators motivated by semiparametric efficiency theory for missing data that better utilize available data. Simulations and a data analysis demonstrate strong performance of the doubly robust survival trees compared with previously used methods. Copyright © 2016 John Wiley & Sons, Ltd.

publication date

  • March 31, 2016

Research

keywords

  • Data Accuracy
  • Models, Statistical
  • Survival Analysis

Identity

PubMed Central ID

  • PMC7286558

Scopus Document Identifier

  • 84979468715

Digital Object Identifier (DOI)

  • 10.1002/sim.6949

PubMed ID

  • 27037609

Additional Document Info

volume

  • 35

issue

  • 20