Estimating Optimal Treatment Regimes from a Classification Perspective. - Wikidata - awgldk - TriplyDB

Estimating Optimal Treatment Regimes from a Classification Perspective.

Estimating Optimal Treatment Regimes from a Classification Perspective.

http://www.wikidata.org/entity/Q36806557

about

Statistical Methods for Establishing Personalized Treatment Rules in Oncology Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review Dynamic Treatment Regimes Greedy outcome weighted tree learning of optimal personalized treatment rules.Doubly Robust Learning for Estimating Individualized Treatment with Censored Data Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials.Regularized outcome weighted subgroup identification for differential treatment effects.Evaluating marker-guided treatment selection strategies.Estimation of optimal dynamic treatment regimes Rejoinder: Combining biomarkers to optimize patient treatment recommendations Discussion of "Combining biomarkers to optimize patient treatment recommendation".Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.On optimal treatment regimes selection for mean survival time.Using decision lists to construct interpretable and parsimonious treatment regimes.Tree-based methods for individualized treatment regimes.A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective.Identifying predictive markers for personalized treatment selection.Flexible functional regression methods for estimating individualized treatment regimes.Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.Characterizing expected benefits of biomarkers in treatment selection.Personalized Dose Finding Using Outcome Weighted Learning.A general statistical framework for subgroup identification and comparative treatment scoring.Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective.Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.Interactive Q-learning for Quantiles.New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.Identifying optimal biomarker combinations for treatment selection through randomized controlled trials.Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials.Identifying optimal biomarker combinations for treatment selection via a robust kernel method.Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.On Estimation of Optimal Treatment Regimes For Maximizing t-Year Survival Probability.A doubly robust estimator for the attributable benefit of a treatment regime.Individual treatment effect prediction for amyotrophic lateral sclerosis patients.Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.C-learning: A new classification framework to estimate optimal dynamic treatment regimes.Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.Optimal treatment assignment to maximize expected outcome with multiple treatments.TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD.

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P2860

Estimating Optimal Treatment Regimes from a Classification Perspective.

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Estimating Optimal Treatment Regimes from a Classification Perspective.

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Anastasios A Tsiatis

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Baqun Zhang

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Eric Laber

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Marie Davidian

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Min Zhang

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P2860

Reinforcement learning design for cancer clinical trials

Demystifying optimal dynamic treatment regimes.

A robust method for estimating optimal treatment regimes.

Estimating Individualized Treatment Rules Using Outcome Weighted Learning

Estimation and extrapolation of optimal treatment and testing strategies.

Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions

Variable Selection for Qualitative Interactions.

A generalized estimator of the attributable benefit of an optimal treatment regime.

Regret-regression for optimal dynamic treatment regimes.

Testing for qualitative interactions between treatment effects and patient subsets.

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103-114

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10.1002/STA.411

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1

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