Dynamic Treatment Regimes

1.1 What Is a Dynamic Treatment Regime? In the context of treatment of a chronic
disease or disorder, a dynamic treatment regime is a set of sequential decision
rules, each corresponding to a key point in the disease or disorder progression ...

Dynamic Treatment Regimes

Author: Anastasios A. Tsiatis

Publisher: CRC Press

ISBN: 1498769780

Page: 602

View: 399

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.

Dynamic Treatment Regimes

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.

Dynamic Treatment Regimes

Author: Marie Davidian

Publisher: CRC Press

ISBN: 9781032082288

Page: 602

View: 557

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

This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data.

Statistical Methods for Dynamic Treatment Regimes

Author: Bibhas Chakraborty

Publisher: Springer Science & Business Media

ISBN: 1461474280

Page: 204

View: 752

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.

G estimation of Dynamic Treatment Regimes in the Presence of Shared Parameters

In this work, I focuson G-estimation, a regression-based approach to estimating the parameters of a DTR, in the specificsetting where treatment decision rule parameters may be shared across different stages of the treatmentsequence.

G estimation of Dynamic Treatment Regimes in the Presence of Shared Parameters

Author: Shouao Wang

Publisher:

ISBN:

Page:

View: 228

"Personalized medicine is gaining attention as a promising avenue for improved healthcare, and hasreceived increased research interest in many domains. A dynamic treatment regime (DTR) is oneapproach to personalized medicine, which has as its basis sequential (in terms of treatment stages)decision rules that are based on a patient's personal, and evolving, medical history. In this work, I focuson G-estimation, a regression-based approach to estimating the parameters of a DTR, in the specificsetting where treatment decision rule parameters may be shared across different stages of the treatmentsequence.In this thesis, a new computational method is introduced to perform shared-parameter G-estimation.The new method shares similar theoretical properties with the original, "unshared" sequential G-estimation:the new approach retains the double-robustness property, which ensures consistent estimation as longas one of (i) the expected treatment-free outcome model or (ii) the treatment model is correctly specified.Simulation studies are conducted to test the validity and performance of the shared G-estimation.In addition, comparisons between unshared and shared Q-learning, unshared sequential G-estimation,and shared-parameter G-estimation are made in terms of bias and variance. The shared parameterG-estimation method is applied to the data from the the STAR*D (NIMH Sequenced Treatment Alternativesto Relieve Depression) randomized trial to estimate the optimal shared-parameter DTR aimedat reducing symptoms of depression." --

Change point Detection for Infinite Horizon Dynamic Treatment Regimes

A dynamic treatment regime is a set of decision rules for how to treat a patient at multiple time points. At each time point, a treatment decision is made depending on the patient’s medical history up to that point.

Change point Detection for Infinite Horizon Dynamic Treatment Regimes

Author:

Publisher:

ISBN:

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View: 174

A dynamic treatment regime is a set of decision rules for how to treat a patient at multiple time points. At each time point, a treatment decision is made depending on the patient’s medical history up to that point. We consider the infinite-horizon setting in which the number of decision points is very large. Specifically, we consider long trajectories of patients’ measurements recorded over time. At each time point, the decision whether to intervene or not is conditional on whether or not there was a change in the patient’s trajectory. We present change-point detection tools and show how to use them in defining dynamic treatment regimes. The performance of these regimes is assessed using an extensive simulation study. We demonstrate the utility of the proposed change-point detection approach using two case studies: detection of sepsis in preterm infants in the intensive care unit and detection of a change in glucose levels of a diabetic patient.

Optimal Treatment Regimes for Personalized Medicine and Mobile Health

The PRO-aLasso estimators are shown to enjoy the same oracle properties as the adaptive Lasso.

Optimal Treatment Regimes for Personalized Medicine and Mobile Health

Author: Eun Jeong Oh

Publisher:

ISBN:

Page:

View: 546

The PRO-aLasso estimators are shown to enjoy the same oracle properties as the adaptive Lasso. Simulations and real data application demonstrate that the PRO-aLasso yields simple, more stable policies with better results as compared to the adaptive Lasso and other competing methods. In Chapter 3, we propose a penalized A-learning with a Lasso-type penalty for the construction of optimal DTR and derive generalization error bounds of the estimated DTR. We first examine the relationship between value and the Q-functions, and then we provide a finite sample upper bound on the difference in values between the optimal DTR and the estimated DTR. In practice, we implement a multi-stage PRO-aLasso algorithm to obtain the optimal DTR. Simulation results show advantages of the proposed methods over some existing alternatives. The proposed approach is also demonstrated with the data from a depression clinical trial study. In Chapter 4, we present future work and concluding remarks.

Adaptive TreatmentStrategies in Practice Planning Trials and Analyzing Data for Personalized Medicine

Observational data allows inference for “viable” dynamic treatment regimes, but
only those which are viable in the specific dataset, limited to the sample size,
patient population, environmental setting, etc. There may be other possible
dynamic ...

Adaptive TreatmentStrategies in Practice  Planning Trials and Analyzing Data for Personalized Medicine

Author: Michael R. Kosorok

Publisher: SIAM

ISBN: 1611974178

Page: 348

View: 665

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.÷

Real World Health Care Data Analysis

12.1 Introduction 12.2 Dynamic Treatment Regimes and Target Trial Emulation
12.2.1 Dynamic Treatment Regimes 12.2.2 Target Trial Emulation 12.3 Example:
Target Trial Approach Applied to the Simulated REFLECTIONS Data 12.3.1 ...

Real World Health Care Data Analysis

Author: Douglas Faries

Publisher: SAS Institute

ISBN: 164295800X

Page: 436

View: 357

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

Value Search Estimators of Individualized Treatment Regimes Using a New Class of Weights

"Personalized medicine is a rapidly growing field of health research.

Value Search Estimators of Individualized Treatment Regimes Using a New Class of Weights

Author: Yuxin Fan

Publisher:

ISBN:

Page:

View: 477

"Personalized medicine is a rapidly growing field of health research. Dynamic treatment regimes (DTRs) are a way of formalizing the sequence of decisions that are made based on the personal medical history. Value search estimators such as inverse probability weighted estimators (IPWE) and augmented inverse probability weighted estimators (AIPWE) are frequently used for estimating DTRs. These estimators directly specify a restricted class of regimes and find the optimal regime by maximizing the expected outcome under each of the regimes in the class. The IPWE is a singly robust estimator which requires the correct specification of the treatment model, however, the AIPWE enjoys double robustness properties: an unbiased estimator is obtained provided at least one of the outcome regression model or treatment model is correctly specified. Recently, a new method of estimating DTRs was proposed, dynamic weighted ordinary least squares (dWOLS) that combines two established methods: Q-learning and G-estimation. In this thesis, instead of using the original inverse probability weights, I propose the use of dWOLS-style weights in singly- and doubly-robust value-search estimators to estimate the optimal DTRs. The new singly-robust estimators with the dWOLS weights are proven to possess the consistency property, whereas the doubly-robust estimators are shown not to achieve consistency. I illustrate the performance of the newly proposed estimation methods through simulation studies and further illustrate them in an analysis of the United States National Health and Nutrition Examination Survey." --

Modern Clinical Trial Analysis

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion.

Modern Clinical Trial Analysis

Author: Wan Tang

Publisher: Springer Science & Business Media

ISBN: 1461443229

Page: 254

View: 698

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion. The intent of the book is to provide an overview of the primary statistical and data analytic issues associated with each of the selected topics, followed by a discussion of approaches for tackling such issues and available software packages for carrying out analyses. While classic topics such as survival data analysis, analysis of diagnostic test data and assessment of measurement reliability are well known and covered in depth by available topic-specific texts, this volume serves a different purpose: it provides a quick introduction to each topic for self-learning, particularly for those who have not done any formal coursework on a given topic but must learn it due to its relevance to their multidisciplinary research. In addition, the chapters on these classic topics will reflect issues particularly relevant to modern clinical trials such as longitudinal designs and new methods for analyzing data from such study designs. The coverage of these topics provides a quick introduction to these important statistical issues and methods for addressing them. As with the classic topics, this part of the volume on modern topics will enable researchers to grasp the statistical methods for addressing these emerging issues underlying modern clinical trials and to apply them to their research studies.

IMS Bulletin

... Jim Berger ( Directorate Liaison ) and Alan Gelfand ( Local Scientific
Coordinator ) . SAMSI will also be conducting two intensive summer research
programs : ( i ) Challenges in Dynamic Treatment Regimes and Multistage
Decision - Making ...

IMS Bulletin

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