Related Experiment Video
Updated: Jul 30, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Hunyong Cho1, Shannon T Holloway2, David J Couper3
1Department of Biostatistics, University of North Carolina, 135 Dauer Drive, Chapel Hill, North Carolina 27599, U.S.A.
Abstract:
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence. Simulations and analysis of the Atherosclerosis Risk in Communities study data suggest that the new estimator brings higher expected outcomes than existing methods in various settings.
Related Concept Videos
Censoring Survival Data
Comparing the Survival Analysis of Two or More Groups
Assumptions of Survival Analysis
Kaplan-Meier Approach
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Cancer Survival Analysis

