Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data
From MaRDI portal
Publication:6628322
DOI10.1002/SIM.9177zbMATH Open1546.62733MaRDI QIDQ6628322
Lu Wang, Michael A. Gorin, Jeremy M. G. Taylor, Ming Tang
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
observational datadynamic treatment regimesmultistage decision-makingpersonalized health caretree-based reinforcement learningtest-and-treat strategy
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
- High-dimensional \(A\)-learning for optimal dynamic treatment regimes
- \({\mathcal Q}\)-learning
- Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme
- Marginal Mean Models for Dynamic Regimes
- Optimal Dynamic Treatment Regimes
- Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer
- Set‐valued dynamic treatment regimes for competing outcomes
- Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models
- Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
This page was built for publication: Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6628322)