Author : Rebecca Sarah Hager
Publisher :
Page : 113 pages
File Size : 21,14 MB
Release : 2016
Category :
ISBN :
Author : S M Ferdous Hossain
Publisher :
Page : pages
File Size : 15,92 MB
Release : 2020
Category :
ISBN :
"In clinical practice, physicians aim to provide the best medical therapy to patients. This may require fine-tuning treatments, or choosing different treatments for two patients who have the same diagnosis due to differences in patient-level characteristics. Using data to develop rules for personalizing treatment strategies is known as precision medicine, or dynamic treatment regimes (DTRs), where the treatment or recommendation of a physician is based on the patients’ history (including past treatments), risk factors, and any other patient-specific information that may be considered to tailor therapeutic decisions. To date, there are relatively few methods that have been proposed for estimating DTRs to optimize censored outcomes. Clearly, flexible and efficient prediction models are desirable, to maximize accuracy in predicting optimal treatments for individual patients while accommodating censored data and complex interactions between patient factors and treatment. The Bayesian additive regression tree (BART) is an attractive framework in this regard as it can provide simple and interpretable treatment decision without knowing the explicit parametric or functional relationship between the outcome and both treatment and covariates. In this thesis, BART is used to individualize treatment assuming a log-normal accelerated failure time (AFT) distribution for the censored outcome in a two-stage clinical problem. The proposed approach was compared with the well-known parametric modelling approach of Q-learning via simulation. In the case of model misspecification, Q-learning approach performed poorly where AFT-BART performed well and improved with increasing sample size. The methods were also applied to registry data in the context of allogeneic hematopoietic cell transplantation, focusing on the question of which class of immunosuppressants to use so as to prevent and treat the development of the acute graft-vs-host disease to maximize disease-free survival in acute myeloid leukemia patients. I conclude that AFT-BART offers great flexiblility and reduced sensitivity to model misspecification"--
Author : Yichuan Zhao
Publisher : Springer Nature
Page : 545 pages
File Size : 12,32 MB
Release :
Category :
ISBN : 3031506901
Author : Michael R. Kosorok
Publisher : SIAM
Page : 348 pages
File Size : 40,81 MB
Release : 2015-12-08
Category : Medical
ISBN : 1611974186
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.
Author : Wan Tang
Publisher : Springer Science & Business Media
Page : 256 pages
File Size : 25,10 MB
Release : 2012-09-05
Category : Medical
ISBN : 1461443229
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.
Author : Anastasios A. Tsiatis
Publisher : CRC Press
Page : 602 pages
File Size : 25,60 MB
Release : 2019-12-19
Category : Mathematics
ISBN : 1498769780
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.
Author : Solanki, Arun
Publisher : IGI Global
Page : 674 pages
File Size : 32,93 MB
Release : 2019-12-13
Category : Computers
ISBN : 1522596453
As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
Author : Bibhas Chakraborty
Publisher : Springer Science & Business Media
Page : 220 pages
File Size : 22,38 MB
Release : 2013-07-23
Category : Medical
ISBN : 1461474280
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.
Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
Page : 483 pages
File Size : 35,42 MB
Release : 2017-09-28
Category : Medical
ISBN : 0309459575
Drug overdose, driven largely by overdose related to the use of opioids, is now the leading cause of unintentional injury death in the United States. The ongoing opioid crisis lies at the intersection of two public health challenges: reducing the burden of suffering from pain and containing the rising toll of the harms that can arise from the use of opioid medications. Chronic pain and opioid use disorder both represent complex human conditions affecting millions of Americans and causing untold disability and loss of function. In the context of the growing opioid problem, the U.S. Food and Drug Administration (FDA) launched an Opioids Action Plan in early 2016. As part of this plan, the FDA asked the National Academies of Sciences, Engineering, and Medicine to convene a committee to update the state of the science on pain research, care, and education and to identify actions the FDA and others can take to respond to the opioid epidemic, with a particular focus on informing FDA's development of a formal method for incorporating individual and societal considerations into its risk-benefit framework for opioid approval and monitoring.
Author :
Publisher :
Page : 902 pages
File Size : 38,35 MB
Release : 2008
Category : Dissertations, Academic
ISBN :
