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Dynamic treatment regimes: Technical challenges and applications
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Outgoing claims: the papers this work cites, builds on, or argues with.
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Comment on “Dynamic treatment regimes: Technical challenges and applications”
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Discussion of “Dynamic treatment regimes: Technical challenges and applications”
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Discussion of “Dynamic treatment regimes: Technical challenges and applications”
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Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy
inferred← citeshttps://doi.org/10.1214/16-aos1534openalex-snapshot
Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward
inferred← citeshttps://doi.org/10.1214/17-ejs1305openalex-snapshot
Tree based weighted learning for estimating individualized treatment rules with censored data
inferred← citeshttps://doi.org/10.1214/18-ejs1480openalex-snapshot
D-learning to estimate optimal individual treatment rules
inferred← citeshttps://doi.org/10.1214/21-aoas1586openalex-snapshot
A marginal structural model for partial compliance in SMARTs
inferred← citeshttps://doi.org/10.1214/21-ba1276openalex-snapshot
Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing
inferred← citeshttps://doi.org/10.1214/22-aos2171openalex-snapshot
Generalization error bounds of dynamic treatment regimes in penalized regression-based learning
inferred← citeshttps://doi.org/10.1214/22-aos2231openalex-snapshot
Batch policy learning in average reward Markov decision processes
inferred← citeshttps://doi.org/10.1214/23-aos2302openalex-snapshot
Projected state-action balancing weights for offline reinforcement learning
inferred← citeshttps://doi.org/10.1214/24-aos2363openalex-snapshot
Finding the optimal dynamic treatment regimes using smooth Fisher consistent surrogate loss
inferred← citeshttps://doi.org/10.1214/25-sts1017openalex-snapshot
Replicable Bandits for Digital Health Interventions
inferred← citeshttps://doi.org/10.1353/obs.2023.a906627openalex-snapshot
Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes
inferred← citeshttps://doi.org/10.1371/journal.pntd.0012881openalex-snapshot
Treatment heterogeneity of water, sanitation, hygiene, and nutrition interventions on child growth by environmental enteric dysfunction and pathogen status for young children in Bangladesh
inferred← citeshttps://doi.org/10.1515/em-2014-0012openalex-snapshot
Discussion of Identification, Estimation and Approximation of Risk under Interventions that Depend on the Natural Value of Treatment Using Observational Data , by Jessica Young, Miguel Hernán, and James Robins
inferred← citeshttps://doi.org/10.1515/jci-2013-0022openalex-snapshot
Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule
inferred← citeshttps://doi.org/10.1515/jci-2020-0012openalex-snapshot
A note on a sensitivity analysis for unmeasured confounding, and the related E-value
inferred← citeshttps://doi.org/10.17615/rwas-sg85openalex-snapshot
Design and Analysis for Precision Medicine Subgroup Identification
inferred← citeshttps://doi.org/10.17709/2410-1893-2022-9-3-9openalex-snapshot
The evolution of personalized medicine: literature review
inferred← citeshttps://doi.org/10.21508/1027-4065-2022-67-4-14-21openalex-snapshot
Personalized medicine: current trends and prospects
inferred← citeshttps://doi.org/10.2196/72472openalex-snapshot
In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems Based on Care Pathway Simulation Models: Scoping Review
inferred← citeshttps://doi.org/10.3390/math11183916openalex-snapshot
Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods
inferred← citeshttps://doi.org/10.3390/stats4040046openalex-snapshot
Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare
inferred← citeshttps://doi.org/10.3758/s13428-024-02470-9openalex-snapshot
Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives
inferred← citeshttps://doi.org/10.3934/math.20261212openalex-snapshot
Proximal policy learning with latent confounding and limited overlap: identification and oracle-calibrated regret bounds
inferred← citeshttps://doi.org/10.48550/arxiv.1810.04338openalex-snapshot
Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals
inferred← citeshttps://doi.org/10.48550/arxiv.1901.00137openalex-snapshot
A Theoretical Analysis of Deep Q-Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1901.00663openalex-snapshot
Efficient augmentation and relaxation learning for individualized treatment rules using observational data
inferred← citeshttps://doi.org/10.48550/arxiv.1906.06646openalex-snapshot
Sample Size Calculations for SMARTs
inferred← citeshttps://doi.org/10.48550/arxiv.1910.05471openalex-snapshot
Uncertainty Quantification and Exploration for Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1911.09260openalex-snapshot
A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity
inferred← citeshttps://doi.org/10.48550/arxiv.2004.01218openalex-snapshot
General Identification of Dynamic Treatment Regimes Under Interference
inferred← citeshttps://doi.org/10.48550/arxiv.2005.10307openalex-snapshot
Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach
inferred← citeshttps://doi.org/10.48550/arxiv.2005.13001openalex-snapshot
Latent-state models for precision medicine
inferred← citeshttps://doi.org/10.48550/arxiv.2007.04445openalex-snapshot
Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score
inferred← citeshttps://doi.org/10.48550/arxiv.2008.02341openalex-snapshot
Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes
inferred← citeshttps://doi.org/10.48550/arxiv.2012.04809openalex-snapshot
Semi-Supervised Off Policy Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2103.01802openalex-snapshot
Median Optimal Treatment Regimes
inferred← citeshttps://doi.org/10.48550/arxiv.2105.04099openalex-snapshot
Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension
inferred← citeshttps://doi.org/10.5691/jjb.43.97openalex-snapshot
Clinical Trials for Personalized Medicine: Design and Data Analysis
inferred← citeshttps://doi.org/10.5691/jjb.47.31openalex-snapshot
Statistical Methods for Constructing Optimal Dynamic Treatment Regime in Sequential Multiple Assignment Randomized Trials
inferred← citeshttps://doi.org/10.5705/ss.202018.0076openalex-snapshot
Entropy Learning for Dynamic Treatment Regimes
inferred← citeshttps://doi.org/10.5705/ss.202018.0451openalex-snapshot
A Sequential Significance Test for Treatment by Covariate Interactions
inferred← citeshttps://doi.org/10.5705/ss.202019.0034openalex-snapshot
DISCUSSION OF "ENTROPY LEARNING FOR DYNAMIC TREATMENT REGIMES"
inferred← citeshttps://doi.org/10.6084/m9.figshare.12851421openalex-snapshot
More Efficient Policy Learning via Optimal Retargeting
inferred← citeshttps://doi.org/10.6339/25-jds1205openalex-snapshot
Reinforcement Learning: A Statistical Perspective
inferred← citeshttps://doi.org/10.70675/9f920116z1378z4232z8590z40226aa8975fopenalex-snapshot
Stratégies de ré-analyse d'un essai de prévention du déclin des fonctions cognitives non concluant
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