# Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring

arXiv: https://arxiv.org/abs/2012.03294 · HTML: https://mlsanity.com/paper/2012.03294/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [Relative Sparsity for Medical Decision Problems](https://mlsanity.com/paper/2211.16566/) (2022)
- [Counterfactual Survival Q-learning via Buckley-James Boosting, with Applications to ACTG 175 and CALGB 8923](https://mlsanity.com/paper/2508.11060/) (2025)
- [AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities](https://mlsanity.com/paper/2311.18725/) (2023)
- [Global Censored Quantile Random Forest](https://mlsanity.com/paper/2410.12209/) (2024)

## Abstract (arXiv)

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 data analysis results suggest that the new estimator brings higher expected outcomes than existing methods in various settings. An R package dtrSurv is available on CRAN.