Dynamic Treatment Regimes

Download Dynamic Treatment Regimes full books in PDF, epub, and Kindle. Read online Dynamic Treatment Regimes ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Dynamic Treatment Regimes

Author : Anastasios A. Tsiatis
Publisher : CRC Press
ISBN 13 : 1498769780
Total Pages : 602 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Dynamic Treatment Regimes by : Anastasios A. Tsiatis

Download or read book Dynamic Treatment Regimes written by Anastasios A. Tsiatis and published by CRC Press. This book was released on 2019-12-19 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.

Statistical Methods for Dynamic Treatment Regimes

Author : Bibhas Chakraborty
Publisher : Springer Science & Business Media
ISBN 13 : 1461474280
Total Pages : 220 pages
Book Rating : 4.4/5 (614 download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods for Dynamic Treatment Regimes by : Bibhas Chakraborty

Download or read book Statistical Methods for Dynamic Treatment Regimes written by Bibhas Chakraborty and published by Springer Science & Business Media. This book was released on 2013-07-23 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies.

Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine

Author : Michael R. Kosorok
Publisher : SIAM
ISBN 13 : 1611974186
Total Pages : 348 pages
Book Rating : 4.6/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine by : Michael R. Kosorok

Download or read book Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine written by Michael R. Kosorok and published by SIAM. This book was released on 2015-12-08 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient's health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine. The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book provides the most up-to-date summary of the current state of the statistical research in personalized medicine; contains chapters by leaders in the area from both the statistics and computer sciences fields; and also contains a range of practical advice, introductory and expository materials, and case studies.

Statistical Remedies for Medical Researchers

Author : Peter F. Thall
Publisher : Springer Nature
ISBN 13 : 3030437140
Total Pages : 297 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!

Book Synopsis Statistical Remedies for Medical Researchers by : Peter F. Thall

Download or read book Statistical Remedies for Medical Researchers written by Peter F. Thall and published by Springer Nature. This book was released on 2020-03-12 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.

Methods for Dynamic Treatment Regimes

Author : Eric James Rose
Publisher :
ISBN 13 :
Total Pages : 97 pages
Book Rating : 4.:/5 (122 download)

DOWNLOAD NOW!

Book Synopsis Methods for Dynamic Treatment Regimes by : Eric James Rose

Download or read book Methods for Dynamic Treatment Regimes written by Eric James Rose and published by . This book was released on 2020 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Reinforcement Learning and Dynamic Programming Using Function Approximators

Author : Lucian Busoniu
Publisher : CRC Press
ISBN 13 : 1439821097
Total Pages : 280 pages
Book Rating : 4.4/5 (398 download)

DOWNLOAD NOW!

Book Synopsis Reinforcement Learning and Dynamic Programming Using Function Approximators by : Lucian Busoniu

Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

Bayesian Designs for Phase I-II Clinical Trials

Author : Ying Yuan
Publisher : CRC Press
ISBN 13 : 1315354225
Total Pages : 233 pages
Book Rating : 4.3/5 (153 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Designs for Phase I-II Clinical Trials by : Ying Yuan

Download or read book Bayesian Designs for Phase I-II Clinical Trials written by Ying Yuan and published by CRC Press. This book was released on 2017-12-19 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time, if promising preclinical results do not translate into a real treatment advance, it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources. Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes. Written by research leaders from the University of Texas MD Anderson Cancer Center, this book shows how Bayesian designs for early-phase clinical trials can explore, refine, and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity.

Proceedings of the Second Seattle Symposium in Biostatistics

Author : Danyu Lin
Publisher : Springer Science & Business Media
ISBN 13 : 1441990763
Total Pages : 332 pages
Book Rating : 4.4/5 (419 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of the Second Seattle Symposium in Biostatistics by : Danyu Lin

Download or read book Proceedings of the Second Seattle Symposium in Biostatistics written by Danyu Lin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a selection of papers presented at the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data. The symposium was held in 2000 to celebrate the 30th anniversary of the University of Washington School of Public Health and Community Medicine. It featured keynote lectures by Norman Breslow, David Cox and Ross Prentice and 16 invited presentations by other prominent researchers. The papers contained in this volume encompass recent methodological advances in several important areas, such as longitudinal data, multivariate failure time data and genetic data, as well as innovative applications of the existing theory and methods. This volume is a valuable reference for researchers and practitioners in the field of correlated data analysis.

Targeted Learning in Data Science

Author : Mark J. van der Laan
Publisher : Springer
ISBN 13 : 3319653040
Total Pages : 640 pages
Book Rating : 4.3/5 (196 download)

DOWNLOAD NOW!

Book Synopsis Targeted Learning in Data Science by : Mark J. van der Laan

Download or read book Targeted Learning in Data Science written by Mark J. van der Laan and published by Springer. This book was released on 2018-03-28 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Design and Analysis of Subgroups with Biopharmaceutical Applications

Author : Naitee Ting
Publisher : Springer Nature
ISBN 13 : 3030401057
Total Pages : 404 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!

Book Synopsis Design and Analysis of Subgroups with Biopharmaceutical Applications by : Naitee Ting

Download or read book Design and Analysis of Subgroups with Biopharmaceutical Applications written by Naitee Ting and published by Springer Nature. This book was released on 2020-05-01 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the theories and applications on subgroups in the biopharmaceutical industry. Drawing from a range of expert perspectives in academia and industry, this collection offers an overarching dialogue about recent advances in biopharmaceutical applications, novel statistical and methodological developments, and potential future directions. The volume covers topics in subgroups in clinical trial design; subgroup identification and personalized medicine; and general issues in subgroup analyses, including regulatory ones. Included chapters present current methods, theories, and case applications in the diverse field of subgroup application and analysis. Offering timely perspectives from a range of authoritative sources, the volume is designed to have wide appeal to professionals in the pharmaceutical industry and to graduate students and researchers in academe and government.

Disease Control Priorities, Third Edition (Volume 6)

Author : King K. Holmes
Publisher : World Bank Publications
ISBN 13 : 1464805253
Total Pages : 506 pages
Book Rating : 4.4/5 (648 download)

DOWNLOAD NOW!

Book Synopsis Disease Control Priorities, Third Edition (Volume 6) by : King K. Holmes

Download or read book Disease Control Priorities, Third Edition (Volume 6) written by King K. Holmes and published by World Bank Publications. This book was released on 2017-11-06 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Infectious diseases are the leading cause of death globally, particularly among children and young adults. The spread of new pathogens and the threat of antimicrobial resistance pose particular challenges in combating these diseases. Major Infectious Diseases identifies feasible, cost-effective packages of interventions and strategies across delivery platforms to prevent and treat HIV/AIDS, other sexually transmitted infections, tuberculosis, malaria, adult febrile illness, viral hepatitis, and neglected tropical diseases. The volume emphasizes the need to effectively address emerging antimicrobial resistance, strengthen health systems, and increase access to care. The attainable goals are to reduce incidence, develop innovative approaches, and optimize existing tools in resource-constrained settings.

Targeted Learning

Author : Mark J. van der Laan
Publisher : Springer Science & Business Media
ISBN 13 : 1441997822
Total Pages : 628 pages
Book Rating : 4.4/5 (419 download)

DOWNLOAD NOW!

Book Synopsis Targeted Learning by : Mark J. van der Laan

Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

List-based Interpretable Dynamic Treatment Regimes

Author : Yichi Zhang
Publisher :
ISBN 13 :
Total Pages : 148 pages
Book Rating : 4.:/5 (954 download)

DOWNLOAD NOW!

Book Synopsis List-based Interpretable Dynamic Treatment Regimes by : Yichi Zhang

Download or read book List-based Interpretable Dynamic Treatment Regimes written by Yichi Zhang and published by . This book was released on 2016 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Optimal Dynamic Treatment Regimes from a Classification Perspective for Two Stage Studies with Survival Data

Author : Rebecca Sarah Hager
Publisher :
ISBN 13 :
Total Pages : 113 pages
Book Rating : 4.:/5 (962 download)

DOWNLOAD NOW!

Book Synopsis Optimal Dynamic Treatment Regimes from a Classification Perspective for Two Stage Studies with Survival Data by : Rebecca Sarah Hager

Download or read book Optimal Dynamic Treatment Regimes from a Classification Perspective for Two Stage Studies with Survival Data written by Rebecca Sarah Hager and published by . This book was released on 2016 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Exposure-Response Modeling

Author : Jixian Wang
Publisher : CRC Press
ISBN 13 : 146657321X
Total Pages : 348 pages
Book Rating : 4.4/5 (665 download)

DOWNLOAD NOW!

Book Synopsis Exposure-Response Modeling by : Jixian Wang

Download or read book Exposure-Response Modeling written by Jixian Wang and published by CRC Press. This book was released on 2015-07-17 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the Latest Statistical Approaches for Modeling Exposure-Response RelationshipsWritten by an applied statistician with extensive practical experience in drug development, Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling, from traditional pharmacokinetic-pharmacody

Nonparametric Models for Longitudinal Data

Author : Colin O. Wu
Publisher : CRC Press
ISBN 13 : 0429939086
Total Pages : 552 pages
Book Rating : 4.4/5 (299 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Models for Longitudinal Data by : Colin O. Wu

Download or read book Nonparametric Models for Longitudinal Data written by Colin O. Wu and published by CRC Press. This book was released on 2018-05-23 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: Provides an overview of parametric and semiparametric methods Shows smoothing methods for unstructured nonparametric models Covers structured nonparametric models with time-varying coefficients Discusses nonparametric shared-parameter and mixed-effects models Presents nonparametric models for conditional distributions and functionals Illustrates implementations using R software packages Includes datasets and code in the authors’ website Contains asymptotic results and theoretical derivations

An Introduction to Causal Inference

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781507894293
Total Pages : 0 pages
Book Rating : 4.8/5 (942 download)

DOWNLOAD NOW!

Book Synopsis An Introduction to Causal Inference by : Judea Pearl

Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.