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Optimization of multi‐stage dynamic treatment regimes utilizing accumulated data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Evaluating multiple treatment courses in clinical trials
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Evaluating multiple treatment courses in clinical trials
inferredcites →https://doi.org/10.1002/sim.2694openalex-snapshot
Estimation of optimal dynamic anticoagulation regimes from observational data: a regret‐based approach
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The control of confounding by intermediate variables
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A dynamic adaptation of the propensity score adjustment for effectiveness analyses of ordinal doses of treatment
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Analytic Methods for Estimating HIV-Treatment and Cofactor Effects
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Optimal Structural Nested Models for Optimal Sequential Decisions
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Causal Inference from Complex Longitudinal Data
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
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The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART)
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Q-learning: A data analysis method for constructing adaptive interventions.
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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer
inferredcites →https://doi.org/10.1080/01621459.2012.695674openalex-snapshot
Estimating Individualized Treatment Rules Using Outcome Weighted Learning
inferredcites →https://doi.org/10.1093/aje/kwm232openalex-snapshot
History-adjusted Marginal Structural Models for Estimating Time-varying Effect Modification
inferredcites →https://doi.org/10.1093/biomet/ast014openalex-snapshot
Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
inferredcites →https://doi.org/10.1097/00001648-200009000-00012openalex-snapshot
Marginal Structural Models to Estimate the Causal Effect of Zidovudine on the Survival of HIV-Positive Men
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Estimation of Direct Causal Effects
inferredcites →https://doi.org/10.1111/1467-985x.00154openalex-snapshot
A Design for Testing Clinical Strategies: Biased Adaptive Within-Subject Randomization
inferredcites →https://doi.org/10.1111/1467-9868.00389openalex-snapshot
Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/j.0006-341x.2002.00048.xopenalex-snapshot
Estimation of Survival Distributions of Treatment Policies in Two‐Stage Randomization Designs in Clinical Trials
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Estimating Optimal Dynamic Regimes: Correcting Bias under the Null
inferredcites →https://doi.org/10.1111/j.1467-9876.2012.01048.xopenalex-snapshot
Evaluating Joint Effects of Induction–Salvage Treatment Regimes on Overall Survival in Acute Leukaemia
inferredcites →https://doi.org/10.1111/j.1541-0420.2005.00377.xopenalex-snapshot
Doubly Robust Estimation in Missing Data and Causal Inference Models
inferredcites →https://doi.org/10.1111/j.1541-0420.2006.00686.xopenalex-snapshot
Demystifying Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/j.1541-0420.2009.01368.xopenalex-snapshot
Regret‐Regression for Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/j.1541-0420.2011.01572.xopenalex-snapshot
Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer
inferredcites →https://doi.org/10.1177/0962280209105013openalex-snapshot
Inference for non-regular parameters in optimal dynamic treatment regimes
inferredcites →https://doi.org/10.1191/1740774504cn002oaopenalex-snapshot
Dynamic treatment regimes: practical design considerations
inferredcites →https://doi.org/10.1198/016214501753382327openalex-snapshot
Marginal Mean Models for Dynamic Regimes
inferredcites →https://doi.org/10.1214/13-sts450openalex-snapshot
$\mathbf{Q}$- and $\mathbf{A}$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.2307/1912934openalex-snapshot
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
inferredcites →https://doi.org/10.48550/arxiv.1006.5831openalex-snapshot
Statistical Inference in Dynamic Treatment Regimes
inferredcites →https://doi.org/10.48550/arxiv.1108.5338openalex-snapshot
Penalized Q-Learning for Dynamic Treatment Regimes
inferredcites →https://doi.org/10.5705/ss.2012.364openalex-snapshot
Penalized Q-learning for dynamic treatment regimens
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4744623openalex-extracted
Reinforcement Learning Using Neural Networks in Estimating an Optimal Dynamic Treatment Regime in Patients with Sepsis: A Retrospective Cohort Study Based on the Mimic-Iii Database
inferred← citeshttps://doi.org/10.1002/sim.7047openalex-snapshot
Identifying cost‐effective dynamic policies to control epidemics
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Step‐adjusted tree‐based reinforcement learning for evaluating nested dynamic treatment regimes using test‐and‐treat observational data
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Multilevel Interventions Targeting Obesity: Research Recommendations for Vulnerable Populations
inferred← citeshttps://doi.org/10.1016/j.cmpb.2025.108754openalex-snapshot
Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis
inferred← citeshttps://doi.org/10.1016/j.urolonc.2024.10.022openalex-snapshot
Impact of postprogression therapies on overall survival: Recommendations from the 2023 kidney cancer association think tank meeting
inferred← citeshttps://doi.org/10.1038/s41598-023-29106-wopenalex-snapshot
Accountable survival contrast-learning for optimal dynamic treatment regimes
inferred← citeshttps://doi.org/10.1080/01621459.2017.1330204openalex-snapshot
Quantile-Optimal Treatment Regimes
inferred← citeshttps://doi.org/10.1080/01621459.2017.1340887openalex-snapshot
A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes
inferred← citeshttps://doi.org/10.1080/01621459.2020.1819294openalex-snapshot
Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes
inferred← citeshttps://doi.org/10.1080/01621459.2021.2008402openalex-snapshot
Fairness-Oriented Learning for Optimal Individualized Treatment Rules
inferred← citeshttps://doi.org/10.1080/14728222.2021.1869212openalex-snapshot
TAM kinase inhibition and immune checkpoint blockade– a winning combination in cancer treatment?
inferred← citeshttps://doi.org/10.1093/biomtc/ujad017openalex-snapshot
Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes
inferred← citeshttps://doi.org/10.1093/jrsssb/qkad016openalex-snapshot
Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes
inferred← citeshttps://doi.org/10.1093/jrsssc/qlad037openalex-snapshot
Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures
inferred← citeshttps://doi.org/10.1111/biom.12539openalex-snapshot
Adaptive Contrast Weighted Learning for Multi-Stage Multi-Treatment Decision-Making
inferred← citeshttps://doi.org/10.1111/biom.13754openalex-snapshot
Estimating Tree-Based Dynamic Treatment Regimes Using Observational Data with Restricted Treatment Sequences
inferred← citeshttps://doi.org/10.1177/0962280217708662openalex-snapshot
Prediction and tolerance intervals for dynamic treatment regimes
inferred← citeshttps://doi.org/10.1177/09622802231206471openalex-snapshot
Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity
inferred← citeshttps://doi.org/10.1186/s12874-024-02333-zopenalex-snapshot
A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.1214/18-aoas1137openalex-snapshot
Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
inferred← citeshttps://doi.org/10.1214/24-aoas1984openalex-snapshot
Censored C-learning for dynamic treatment regime in colorectal cancer study
inferred← citeshttps://doi.org/10.3390/cancers15194674openalex-snapshot
Interpreting Randomized Controlled Trials
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123658openalex-snapshot
Additional file 1 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123661openalex-snapshot
Additional file 2 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123664openalex-snapshot
Additional file 3 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123667openalex-snapshot
Additional file 4 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123670openalex-snapshot
Additional file 5 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123673openalex-snapshot
Additional file 6 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123676openalex-snapshot
Additional file 7 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.6084/m9.figshare.27123679openalex-snapshot
Additional file 8 of A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
inferred← citeshttps://doi.org/10.7302/36openalex-snapshot
Synthetic Data Sharing and Estimation of Viable Dynamic Treatment Regimes with Observational Data
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