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Activity Number: 58 - New Topics in Survival Analysis
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #309771
Title: Interval Censored Recursive Forests
Author(s): Hunyong Cho* and Nicholas P Jewell and Michael R. Kosorok
Companies: and London School of Hygiene &Tropical Medicine and University of North Carolina at Chapel Hill
Keywords: survival analysis; random forest; interval censoring; self-consistency; machine learning

We propose the interval censored recursive forests (ICRF) which is an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator makes the best use of censored information by iteratively updating the survival estimate, and can be viewed as a self-consistent estimator with convergence monitored using out-of-bag samples. Splitting rules optimized for interval censored data are developed and kernel-smoothing is applied. The ICRF displays the highest prediction accuracy among competing nonparametric methods in most of the simulations and in an applied example to avalanche data. An R package icrf is available for implementation.

Authors who are presenting talks have a * after their name.

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