Download or read book Evaluating Optimal Individualized Treatment Rules written by Alexander Ryan Luedtke and published by . This book was released on 2016 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Suppose we observe baseline covariates, a binary indicator of treatment, and an outcome occuring after treatment. An individualized treatment rule (ITR) is a treatment rule which assigns treatments to individuals based on their measured covariates. An optimal ITR is the ITR which maximizes the population mean outcome. The mean outcome of the optimal ITR is referred to as the optimal value. This dissertation considers three inferential challenges related to these parameters in the large semiparametric model that at most places restrictions on the probability of receiving treatment given covariates. The first is to develop confidence intervals for the optimal value. Constructing valid confidence intervals for this quantity is surprisingly difficult when the stratum specific treatment effect, also called the blip function, is null with positive probability. This null treatment effect seems possible in many studies. While it has been claimed in the literature that no regular and asymptotically linear (RAL) estimator exists in this case, we prove that RAL estimators of the optimal value can exist in a slightly more general setting. We then describe an approach to obtain root-n rate confidence intervals for the optimal value even when regular estimation is not possible. We also provide sufficient conditions under which our estimator is RAL and asymptotically efficient -- a necessary condition is of course that regular estimation is possible under the data generating distribution. We have thus far assumed that treatment is an unlimited resource so that the entire population can be treated if this strategy maximizes the population mean outcome. In the second part of this dissertation, we consider optimal ITRs in settings where the treatment resource is limited so that there is a maximum proportion of the population that can be treated. We give a general closed-form expression for an optimal stochastic ITR in this resource-limited setting, and a closed-form expression for the optimal deterministic ITR under an additional assumption. We also present an estimator of the mean outcome under the optimal stochastic ITR and give conditions under which our estimator is efficient among all RAL estimators. Both of the first two inferential challenges considered give parametric-rate confidence intervals for finite-dimensional parameters in our large semiparametric model. In the third part of this dissertation we focus on developing hypothesis tests and confidence sets for infinite-dimensional parameters that one typically estimates using data adaptive techniques. Parametric-rate inference is not typically expected in this setting. Our primary motivating example concerns the blip function, which is closely related to the optimal ITRs in both the resource-unconstrained and constrained settings. For any fixed function, we give valid hypothesis tests that the blip function is equal to this fixed function. These tests can then be inverted to develop a confidence set for the blip function. Surprisingly, the hypothesis test achieves a parametric rate in the sense that it is consistent against local alternatives converging to the data generating distribution at the rate of one divided by the square root of sample size. We prove the validity of this procedure in great generality that applies far beyond this particular inference problem, and reference several other examples to which it applies. The results in this third component of the dissertation have been developed using the theory of higher-order influence functions.
Download or read book Tree based Ensemble Methods for Individualized Treatment Rules written by Kehao Zhu and published by . This book was released on 2016 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a growing interest in statistical methods for the personalized medicine or precision medicine, especially for deriving optimal individualized treatment rules (ITRs). An ITR recommends a patient to a treatment based on the patient's characteristics. The common parametric methods for deriving optimal ITR, which model the clinical endpoint as a function of the patient's characteristics in the first step, can have suboptimal performance when the conditional mean model is misspecified. Recent methodology development has cast the problem of deriving optimal ITR under a weighted classification framework. Under this weighted classification framework, we develop a weighted random forests (W-RF) algorithm that derives an optimal ITR nonparametrically. In addition, with the W-RF algorithm, we propose the variable importance measures for quantifying relative relevance of the patient's characteristics to treatment selection, and the out-of-bag estimator for the population aver- age outcome under the estimated optimal ITR. Our proposed methods are evaluated through intensive simulation studies. We apply our methods to data from Clinical Antipsychotic Trials of Intervention Effectiveness Alzheimer's Disease Study (CATIE-AD) as an illustration.
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.
Download or read book Optimal Individualized Treatment Strategy written by Shikai Luo and published by . This book was released on 2016 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistics in Precision Health written by Yichuan Zhao and published by Springer Nature. This book was released on with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Estimating a Cost effective Individualized Treatment Rule CE ITR Based on Machine Learning written by Qing Zhang and published by . This book was released on 2022 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policy makers employ Cost-Effectiveness Analysis (CEA) to evaluate a new treatment based on its cost and effectiveness. ITR is the treatment recommendation based on patient's characteristics. However, the recommends generated from ITR and CEA could mismatch, even opposite since their aim is different. Therefore, policy makers need a tool to trade-off between ITR and CEA. Traditionally, optimal ITR focus on the mean benefit on population level, not on individual level. In the era of precision medicine, an ideal intervention needs to be optimized based on individual level.
Download or read book Evaluating the Use of Generalized Dynamic Weighted Ordinary Least Squares for Individualized HIV Treatment Strategies written by Larry Dong and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Dynamic treatment regimes (DTR) are a statistical paradigm in precision medicine which aim to optimize patient outcomes by individualizing treatments. At its simplest, a DTR may require only a single decision to be made; this special case is called an individualized treatment rule (ITR) and is often used to maximize short-term rewards. Generalized dynamic weighted ordinary least squares (G-dWOLS), a DTR estimation method that offers theoretical advantages such as double robustness of parameter estimators in the decision rules, has been recently extended to now accommodate categorical treatments. In this work, G-dWOLS is applied to longitudinal data to estimate an optimal ITR, which is demonstrated in simulations. This novel method is then applied to a population affected by HIV whereby an ITR for the administration of interleukin 7 (IL-7) is devised to maximize the duration where the CD4 load is above a healthy threshold (550 cells/μL) while preventing the administration of unnecessary injections"--
Download or read book Handbook of Statistical Methods for Precision Medicine written by Eric Laber and published by CRC Press. This book was released on 2024-10-23 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical study and development of analytic methodology for individualization of treatments is no longer in its infancy. Many methods of study design, estimation, and inference exist, and the tools available to the analyst are ever growing. This handbook introduces the foundations of modern statistical approaches to precision medicine, bridging key ideas to active lines of current research in precision medicine. The contributions in this handbook vary in their level of assumed statistical knowledge; all contributions are accessible to a wide readership of statisticians and computer scientists including graduate students and new researchers in the area. Many contributions, particularly those that are more comprehensive reviews, are suitable for epidemiologists and clinical researchers with some statistical training. The handbook is split into three sections: Study Design for Precision Medicine, Estimation of Optimal Treatment Strategies, and Precision Medicine in High Dimensions. The first focuses on designed experiments, in many instances, building and extending on the notion of sequential multiple assignment randomized trials. Dose finding and simulation-based designs using agent-based modelling are also featured. The second section contains both introductory contributions and more advanced methods, suitable for estimating optimal adaptive treatment strategies from a variety of data sources including non-experimental (observational) studies. The final section turns to estimation in the many-covariate setting, providing approaches suitable to the challenges posed by electronic health records, wearable devices, or any other settings where the number of possible variables (whether confounders, tailoring variables, or other) is high. Together, these three sections bring together some of the foremost leaders in the field of precision medicine, offering new insights and ideas as this field moves towards its third decade.
Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
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.
Download or read book Registries for Evaluating Patient Outcomes written by Agency for Healthcare Research and Quality/AHRQ and published by Government Printing Office. This book was released on 2014-04-01 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
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.
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.
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 354 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.?
Download or read book Biopharmaceutical Applied Statistics Symposium written by Karl E. Peace and published by Springer. This book was released on 2018-09-03 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become the premier international conference in biopharmaceutical statistics. The primary aims of the BASS are: 1) to raise funding to support graduate students in biostatistics programs, and 2) to provide an opportunity for professionals engaged in pharmaceutical drug research and development to share insights into solving the problems they encounter. The BASS book series is initially divided into three volumes addressing: 1) Design of Clinical Trials; 2) Biostatistical Analysis of Clinical Trials; and 3) Pharmaceutical Applications. This book is the third of the 3-volume book series. The topics covered include: Targeted Learning of Optimal Individualized Treatment Rules under Cost Constraints, Uses of Mixture Normal Distribution in Genomics and Otherwise, Personalized Medicine – Design Considerations, Adaptive Biomarker Subpopulation and Tumor Type Selection in Phase III Oncology Trials, High Dimensional Data in Genomics; Synergy or Additivity - The Importance of Defining the Primary Endpoint, Full Bayesian Adaptive Dose Finding Using Toxicity Probability Interval (TPI), Alpha-recycling for the Analyses of Primary and Secondary Endpoints of Clinical Trials, Expanded Interpretations of Results of Carcinogenicity Studies of Pharmaceuticals, Randomized Clinical Trials for Orphan Drug Development, Mediation Modeling in Randomized Trials with Non-normal Outcome Variables, Statistical Considerations in Using Images in Clinical Trials, Interesting Applications over 30 Years of Consulting, Uncovering Fraud, Misconduct and Other Data Quality Issues in Clinical Trials, Development and Evaluation of High Dimensional Prognostic Models, and Design and Analysis of Biosimilar Studies.
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.
Download or read book The AHA Guidelines and Scientific Statements Handbook written by Valentin Fuster and published by John Wiley & Sons. This book was released on 2011-09-23 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: Society-sanctioned guidelines on care are valuable tools, but accessing key information from the often complicated statements has been a daunting task. Now, practitioners and their institutions have a clear path to successful application of guidelines from the American Heart Association. This book outlines the key AHA guidelines, Statements, and Performance Measures and includes comparisons with the associated European guidelines. This book also has a strong online component, which will alert users who sign up to new updates to the guidelines and other relevant information. It will also have links through to the full guidelines and statements.
