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| Author | : Baqun Zhang |
| Publisher | : |
| Total Pages | : 77 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
| Author | : Anastasios A. Tsiatis |
| Publisher | : CRC Press |
| Total Pages | : 602 |
| Release | : 2019-12-19 |
| Genre | : 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 | : Phillip Joel Schulte |
| Publisher | : |
| Total Pages | : 103 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
| Author | : Michael R. Kosorok |
| Publisher | : SIAM |
| Total Pages | : 348 |
| Release | : 2015-12-08 |
| Genre | : 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 | : Bibhas Chakraborty |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 220 |
| Release | : 2013-07-23 |
| Genre | : 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 | : Yuxin Fan |
| Publisher | : |
| Total Pages | : |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
"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." --
| Author | : Yichuan Zhao |
| Publisher | : Springer Nature |
| Total Pages | : 545 |
| Release | : |
| Genre | : |
| ISBN | : 3031506901 |
| Author | : KyungMann Kim |
| Publisher | : CRC Press |
| Total Pages | : 655 |
| Release | : 2021-08-23 |
| Genre | : Mathematics |
| ISBN | : 1498714641 |
Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.
| Author | : Danyu Lin |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 332 |
| Release | : 2012-12-06 |
| Genre | : Medical |
| ISBN | : 1441990763 |
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.
| Author | : Mark J. van der Laan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 628 |
| Release | : 2011-06-17 |
| Genre | : Mathematics |
| ISBN | : 1441997822 |
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.
