Relevant bibliographies by topics / Dynamic treatment regimes

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dynamic treatment regimes'

Author: Grafiati
Published: 23 November 2024
Last updated: 30 November 2024

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Journal articles on the topic "Dynamic treatment regimes"

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Chakraborty, Bibhas, and Susan A. Murphy. "Dynamic Treatment Regimes." Annual Review of Statistics and Its Application 1, no. 1 (2014): 447–64. http://dx.doi.org/10.1146/annurev-statistics-022513-115553.

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Lavori, Philip W., and Ree Dawson. "Dynamic treatment regimes: practical design considerations." Clinical Trials 1, no. 1 (2004): 9–20. http://dx.doi.org/10.1191/1740774504cn002oa.

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Background Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (“sequential ignorability”). In longitudinal observational studies, sequential ignorability must be assumed, while ran
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Murphy, S. A. "Optimal dynamic treatment regimes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 2 (2003): 331–55. http://dx.doi.org/10.1111/1467-9868.00389.

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Zhang, Yichi, Eric B. Laber, Marie Davidian, and Anastasios A. Tsiatis. "Interpretable Dynamic Treatment Regimes." Journal of the American Statistical Association 113, no. 524 (2018): 1541–49. http://dx.doi.org/10.1080/01621459.2017.1345743.

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Moodie, Erica E. M., Thomas S. Richardson, and David A. Stephens. "Demystifying Optimal Dynamic Treatment Regimes." Biometrics 63, no. 2 (2007): 447–55. http://dx.doi.org/10.1111/j.1541-0420.2006.00686.x.

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Zhao, Ying-Qi, and Eric B. Laber. "Estimation of optimal dynamic treatment regimes." Clinical Trials: Journal of the Society for Clinical Trials 11, no. 4 (2014): 400–407. http://dx.doi.org/10.1177/1740774514532570.

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Lavori, Philip W., and Ree Dawson. "Dynamic treatment regimes: practical design considerations." Clinical Trials 1, no. 1 (2004): 9–20. http://dx.doi.org/10.1191/1740774s04cn002oa.

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Johnson, Brent A. "Treatment-competing events in dynamic regimes." Lifetime Data Analysis 14, no. 2 (2007): 196–215. http://dx.doi.org/10.1007/s10985-007-9051-3.

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Lizotte, Daniel J., and Arezoo Tahmasebi. "Prediction and tolerance intervals for dynamic treatment regimes." Statistical Methods in Medical Research 26, no. 4 (2017): 1611–29. http://dx.doi.org/10.1177/0962280217708662.

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We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact tha
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Murphy, S. A., and D. Bingham. "Screening Experiments for Developing Dynamic Treatment Regimes." Journal of the American Statistical Association 104, no. 485 (2009): 391–408. http://dx.doi.org/10.1198/jasa.2009.0119.

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Dissertations / Theses on the topic "Dynamic treatment regimes"

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Moodie, Erica E. M. "Inference for optimal dynamic treatment regimes /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9605.

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Mohamed, Nur Anisah. "Optimal dynamic treatment regimes : regret-regression method with myopic strategies." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2242.

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Optimal dynamic treatment strategies provide a set of decision rules that are based on a patient’s history. We assume there are a sequence of decision times j = 1,2,...,K. At each time a measurement of the state of the patient Sj is obtained and then some action Aj is decided. The aim is to provide rules for action choice so as to maximise some final value Y . In this thesis we will focus on the regret-regression method described by Henderson et al. (2009), and the regret approach to optimal dynamic treatment regimes proposed by Murphy (2003). The regret-regression method combines the regret f
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Yazzourh, Sophia. "Apprentissage par renforcement et outcome-weighted learning bayésien pour la médecine de précision : Intégration de connaissances médicales dans les algorithmes de décision." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES139.

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La médecine de précision vise à adapter les traitements aux caractéristiques de chaque patient en s'appuyant sur les formalismes des "Individualized Treatment Regimes" (ITR) et des "Dynamic Treatment Regimes" (DTR). Les ITR concernent une seule décision thérapeutique, tandis que les DTR permettent l'adaptation des traitements au fil du temps via une séquence de décisions. Pour être pertinentes, ces approches doivent être en mesure de traiter des données complexes et d'intégrer les connaissances médicales, essentielles pour permettre une utilisation clinique réaliste et sans risques. Cette thès
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Young, Katherine W. "Dynamic treatment regimens for congestive heart failure." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129847.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 59-60).<br>Each year, millions of patients are hospitalized with a diagnosis of congestive heart failure (CHF). This condition is characterized by inadequate tissue perfusion resulting from an inability of the heart to provide enough blood to meet the body's metabolic demands. Patients with CHF remain at a greater risk for mortality and other adverse events. A primary sy
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Zhao, Pan. "Topics in causal inférence and policy learning with applications to precision medicine." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS029.

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La causalité est un concept fondamental en science et en philosophie. Dans un contexte où la collecte massive de données de grande complexité s’impose dans tous les domaines, les statistiques jouent un rôle crucial dans l'inférence des causes et des effets. Cette thèse explore des méthodes avancées d'inférence causale. Elle met l'accent sur l'apprentissage de politiques d’action (“politiques” dans la suite), les variables instrumentales (IV), et les approches de différences en différences (DiD).Les méthodes IV et DiD sont utilisées par les chercheurs en épidémiologie, médecine, biostatistique,
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Rich, Benjamin. "Optimal dynamic treatment regime structural nested mean models: improving efficiency through diagnostics and re-weighting and application to adaptive individual dosing." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114179.

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Dynamic treatment regimes are common in medicine, for example in the treatment of chronic diseases. As information about a patient is gathered over time, it is desirable to make use of this accumulating information to make treatment decisions that are specifically tailored to the individual patient, or to base decisions on dynamically evolving observations. Dynamic treatment regimes have been the topic of much recent work in the area of causal inference. In particular, semi-parametric methods for estimating a "best" or "optimal" treatment rule or strategy from observational data have been deve
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Daddi, Hammou Aoumeur. "Improved theoretical treatment of the dynamics of quarkonia in the quark gluon plasma : from semiclassical approximation to unified quantum master equations between the quantum Brownian and the quantum optical regimes." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0425.

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Les quarkonia lourds sont l'une des sondes du plasma de quarks et de gluons (QGP) formé dans les collisions ultrarelativistes d'ions lourds. Au cours de la dernière décennie, l'utilisation du formalisme des systèmes quantiques ouverts (SQO) dans l'étude de la dynamique des quarkonia dans le QGP a suscité un intérêt croissant. En particulier, il a été montré que l'équation de Lindblad peut donner lieu à des équations semi-classiques, qui ont été précédemment utilisées par plusieurs modèles phénoménologiques. Dans une première partie de cette thèse, nous étudions la validité de l'approximation s
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Mohammadighavam, S. (Shahram). "Hydrological and hydraulic design of peatland drainage and water treatment systems for optimal control of diffuse pollution." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526214511.

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Abstract Peatland drainage for forestry, agriculture and peat extraction results in runoff rich in organic matter, sediments and nutrients. This has a significant effect on downstream ecosystems. Therefore, water purification using sedimentation basins and wetlands is required in environmental permits for peat extraction in Finland, to reduce downstream impacts. Due to increasingly strict environmental regulations, more advanced water purification methods need to be developed. Using field measurements, laboratory experiments and hydrological/hydraulic modelling, this thesis sought to develop n
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DELIU, NINA. "Reinforcement learning in modern biostatistics: benefits, challenges and new proposals." Doctoral thesis, 2021. http://hdl.handle.net/11573/1581572.

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Applications of reinforcement learning (RL) for supporting, managing and improving decision-making are becoming increasingly popular in a variety of medicine and healthcare domains where the problem has a sequential nature. By continuously interacting with the underlying environment, RL techniques are able to learn by trial-and-error on how to take better actions in order to maximize an outcome of interest over time. However, if on one hand RL offers a new powerful framework, on the other hand it poses some unique challenges for data analysis and interpretability, which call for new statistica
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Books on the topic "Dynamic treatment regimes"

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692.

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Chakraborty, Bibhas, and Erica E. M. Moodie. Statistical Methods for Dynamic Treatment Regimes. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7428-9.

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Chakraborty, Bibhas. Statistical methods for dynamic treatment regimes: Reinforcement learning, causal inference, and personalized medicine. Springer, 2013.

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Tsiatis, Anastasios A. Dynamic Treatment Regimes. Taylor & Francis Group, 2021.

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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric Laber. Introduction to Dynamic Treatment Regimes. Taylor & Francis Group, 2019.

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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.

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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.

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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.

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Moodie, Erica E. M., and Bibhas Chakraborty. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer New York, 2015.

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Orellana, Liliana del Carmen. Methodological challenges for the estimation of optimal dynamic treatment regimes from observational studies. 2007.

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Book chapters on the topic "Dynamic treatment regimes"

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Qian, Min, Inbal Nahum-Shani, and Susan A. Murphy. "Dynamic Treatment Regimes." In Modern Clinical Trial Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4322-3_5.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Introduction." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-1.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Statistical Inference." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-10.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Additional Topics." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-11.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Preliminaries." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-2.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Single Decision Treatment Regimes: Fundamentals." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-3.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Single Decision Treatment Regimes: Additional Methods." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-4.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Multiple Decision Treatment Regimes: Overview." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-5.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Multiple Decision Treatment Regimes: Formal Framework." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-6.

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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Optimal Multiple Decision Treatment Regimes." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-7.

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Conference papers on the topic "Dynamic treatment regimes"

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Jain, Nishan, and James Baeder. "Assessment of Turbulence Model Length Scales based on Hybrid RANS-LES Modeling of Unsteady Flow Over Airfoil." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11393.

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Current work investigates the sensitivity of DES type hybrid methods toward turbulence length scales and anisotropy of grid. Different length scales available from literature are implemented into a compressible, finite volume GPU accelerated Navier-Stokes solver. A novel length scale is proposed based on the properties of available length scales and the grid requirements in mildly separated flows. Predictive capabilities of RANS and DDES method with the conventional and proposed length scales is assessed by conducting static and dynamic stall simulations on SC1095 and modified VR12 airfoils. T
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Wang, Lu, Wenchao Yu, Xiaofeng He, et al. "Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes✱." In WWW '20: The Web Conference 2020. ACM, 2020. http://dx.doi.org/10.1145/3366423.3380248.

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Jiang, Yushan, Wenchao Yu, Dongjin Song, Wei Cheng, and Haifeng Chen. "Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation." In 2023 57th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2023. http://dx.doi.org/10.1109/ciss56502.2023.10089648.

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Liu, Ying, Brent Logan, Ning Liu, Zhiyuan Xu, Jian Tang, and Yangzhi Wang. "Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data." In 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.45.

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Yin, Changchang, Ruoqi Liu, Jeffrey Caterino, and Ping Zhang. "Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539413.

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Li, Mingyi, Xiao Zhang, Haochao Ying, Yang Li, Xu Han, and Dongxiao Yu. "Data Quality Aware Hierarchical Federated Reinforcement Learning Framework for Dynamic Treatment Regimes." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00131.

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Andryushchenko, Aleksei Dmitrievitch. "Halite Precipitation in Brine Reservoirs: Prediction and Control by Numerical Model, Optimization of the Fresh Water Treatments and Well Production Regimes." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206645-ms.

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Abstract The aim of the work is to optimize the fresh water treatment design, frequency and production regimes (maximize NPV of the well operation) for wells with high NaCl content formation water (brines) production, which are very common for the Eastern Siberia, and forecast productivity index (PI) decline rates and production profiles for the wells by means of halite deposition model for brine flow in porous media united with fresh water treatment model and economic model. New numerical halite deposition model for brine flow in porous media is developed based on Darcy's law and equation of
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McDonald, Dale B., and Joseph O. Falade. "Parameter Identification in Ecological Systems via Discontinuous and Singular Control Regimes." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86063.

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Policy decisions regarding commercial harvesting of aquatic species by (typically governmental) regulatory agencies are often based in part upon field data, simulation results, and mathematical models. Regulatory agencies may limit or expand seasons, determine total harvest allowed, increase or decrease licensure fees, and raise or lower taxation rates in response to the state of the ecological system. Ultimately, the regulatory agency uses such measures to ensure viable populations in an attempt to balance ecosystem health and benefits for society. Such decisions impact commercial fishing ven
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Aguiar, Janaina I. S., Antonio A. Pontifes, Jonathan Rogers, and Amir Mahmoudkhani. "Selecting a Product for Wax Remediation: From Characterization of Field Wax Deposits to Improvement of Treatment Sustainability." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30951-ms.

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Abstract Wax deposition is one of the known challenges of flow assurance management in upstream oil production and operations demanding continuous improvements and the search for more effective prevention and remediation methods. At the same time, there are no universally agreed upon test methods to evaluate the efficiency and mechanisms related to the chemical treatments. The objective of this paper is to present and debate different methods to evaluate the effectiveness of batch treatments for remediation of wax deposits and compare commonly applied solvents with fluids containing biosurfact
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Pacaldo, Renato S., Miraç Aydın, and Randell Keith Amarille. "Soil CO2 Effluxes in Post-fire and Undisturbed Pinus nigra Forests: A Soil Moisture Manipulation Study." In 3rd International Congress on Engineering and Life Science. Prensip Publishing, 2023. http://dx.doi.org/10.61326/icelis.2023.41.

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Climate change impacts are driving hydrological extremes and frequent occurrences of forest fires. Whether these impacts result in dramatic changes in the soil CO2 efflux (FCO2) remains poorly understood. This study seeks to understand the changes in the soil FCO2 in recently burned forest (post-fire) and an undisturbed black pine (Pinus nigra, Arnold) forest in Türkiye. A field experiment in a three-way factorial randomized complete block design experiment was established with four replications and three factors; shaded (west) and exposed (east), types of forest fires (surface, crown, and con
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Reports on the topic "Dynamic treatment regimes"

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Neugebauer, Romain, Julie Schmittdiel, Oleg Sofrygin, Alyce Adams, Richard Grant, and Mark van der Laan. Methods to Assess the Effect of Dynamic Treatment Regimens Using Electronic Health Records. Patient-Centered Outcomes Research Institute (PCORI), 2020. http://dx.doi.org/10.25302/06.2020.me.140312506.

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Banin, Amos, Joseph Stucki, and Joel Kostka. Redox Processes in Soils Irrigated with Reclaimed Sewage Effluents: Field Cycles and Basic Mechanism. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7695870.bard.

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The overall objectives of the project were: (a) To measure and study in situ the effect of irrigation with reclaimed sewage effluents on redox processes and related chemical dynamics in soil profiles of agricultural fields. (b) To study under controlled conditions the kinetics and equilibrium states of selected processes that affect redox conditions in field soils or that are effected by them. Specifically, these include the effects on heavy metals sorption and desorption, and the effect on pesticide degradation. On the basis of the initial results from the field study, increased effort was de
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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdra
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Anderson, Donald M., Lorraine C. Backer, Keith Bouma-Gregson, et al. Harmful Algal Research & Response: A National Environmental Science Strategy (HARRNESS), 2024-2034. Woods Hole Oceanographic Institution, 2024. http://dx.doi.org/10.1575/1912/69773.

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Harmful and toxic algal blooms (HABs) are a well-established and severe threat to human health, economies, and marine and freshwater ecosystems on all coasts of the United States and its inland waters. HABs can comprise microalgae, cyanobacteria, and macroalgae (seaweeds). Their impacts, intensity, and geographic range have increased over past decades due to both human-induced and natural changes. In this report, HABs refers to both marine algal and freshwater cyanobacterial events. This Harmful Algal Research and Response: A National Environmental Science Strategy (HARRNESS) 2024-2034 plan bu
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Optional dynamic treatment regimes and partial welfare ordering. Cemmap, 2020. http://dx.doi.org/10.47004/wp.cem.2020.5020.

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