Applied Adaptive Statistical Methods PDF Download
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| Author | : Thomas W. O'Gorman |
| Publisher | : SIAM |
| Total Pages | : 187 |
| Release | : 2004-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780898718430 |
Download Applied Adaptive Statistical Methods Book in PDF, ePub and Kindle
Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general than earlier methods and sufficient software has been developed to make adaptive tests easy to use for many real-world problems. Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a comprehensive approach to tests of significance and confidence intervals. It shows how to make confidence intervals shorter and how to make tests of significance more powerful by using the data itself to select the most appropriate procedure.
| Author | : Michael R. Kosorok |
| Publisher | : SIAM |
| Total Pages | : 348 |
| Release | : 2015-12-08 |
| Genre | : Medical |
| ISBN | : 1611974186 |
Download Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine Book in PDF, ePub and Kindle
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 | : Jelena Markovic |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Download Statistical Methods for Adaptive Data Analysis Book in PDF, ePub and Kindle
We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection procedure. After defining the selected parameters, the conditional correction for selection requires knowledge of how the selection is affected by changes in the underlying data. We address two important issues arising in selective inference methodology: statistical power of selective inference methods and generality of the selection procedures addressed by the methods. We provide two methods that improve on the power of the original selective inference methods. The first way to improve statistical power after data exploration is to do selection on a noisy version of the data, thus using less information in selection and leaving more for inference. We also introduce the bootstrap version of this method and prove asymptotic guarantees. By redefining the selected parameters to require as little as possible information from selection, the second method we introduce here improves greatly on the power of the original selective inference methods. We apply the method to conduct powerful inference after Lasso in high-dimensional settings. The third method enables inference after black box model selection algorithms, without having explicit selection. In this work, we assume we have in silico access to the selection algorithm. We recast the inference problem into a statistical learning problem which can be fit with off-the-shelf models for binary regression. We apply this method to stability selection, which was previously out of reach of this conditional approach.
| Author | : Gernot Wassmer |
| Publisher | : Springer |
| Total Pages | : 310 |
| Release | : 2016-07-04 |
| Genre | : Medical |
| ISBN | : 3319325620 |
Download Group Sequential and Confirmatory Adaptive Designs in Clinical Trials Book in PDF, ePub and Kindle
This book provides an up-to-date review of the general principles of and techniques for confirmatory adaptive designs. Confirmatory adaptive designs are a generalization of group sequential designs. With these designs, interim analyses are performed in order to stop the trial prematurely under control of the Type I error rate. In adaptive designs, it is also permissible to perform a data-driven change of relevant aspects of the study design at interim stages. This includes, for example, a sample-size reassessment, a treatment-arm selection or a selection of a pre-specified sub-population. Essentially, this adaptive methodology was introduced in the 1990s. Since then, it has become popular and the object of intense discussion and still represents a rapidly growing field of statistical research. This book describes adaptive design methodology at an elementary level, while also considering designing and planning issues as well as methods for analyzing an adaptively planned trial. This includes estimation methods and methods for the determination of an overall p-value. Part I of the book provides the group sequential methods that are necessary for understanding and applying the adaptive design methodology supplied in Parts II and III of the book. The book contains many examples that illustrate use of the methods for practical application. The book is primarily written for applied statisticians from academia and industry who are interested in confirmatory adaptive designs. It is assumed that readers are familiar with the basic principles of descriptive statistics, parameter estimation and statistical testing. This book will also be suitable for an advanced statistical course for applied statisticians or clinicians with a sound statistical background.
| Author | : Scott M. Berry |
| Publisher | : CRC Press |
| Total Pages | : 316 |
| Release | : 2010-07-19 |
| Genre | : Mathematics |
| ISBN | : 1439825513 |
Download Bayesian Adaptive Methods for Clinical Trials Book in PDF, ePub and Kindle
Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adapti
| Author | : Anthony C Atkinson |
| Publisher | : CRC Press |
| Total Pages | : 341 |
| Release | : 2013-12-26 |
| Genre | : Mathematics |
| ISBN | : 1584886935 |
Download Randomised Response-Adaptive Designs in Clinical Trials Book in PDF, ePub and Kindle
Randomised Response-Adaptive Designs in Clinical Trials presents methods for the randomised allocation of treatments to patients in sequential clinical trials. Emphasizing the practical application of clinical trial designs, the book is designed for medical and applied statisticians, clinicians, and statisticians in training. After introducing clinical trials in drug development, the authors assess a simple adaptive design for binary responses without covariates. They discuss randomisation and covariate balance in normally distributed responses and cover many important response-adaptive designs for binary responses. The book then develops response-adaptive designs for continuous and longitudinal responses, optimum designs with covariates, and response-adaptive designs with covariates. It also covers response-adaptive designs that are derived by optimising an objective function subject to constraints on the variance of estimated parametric functions. The concluding chapter explores future directions in the development of adaptive designs.
| Author | : Michael R. Kosorok |
| Publisher | : SIAM |
| Total Pages | : 354 |
| Release | : 2015-12-08 |
| Genre | : Medical |
| ISBN | : 1611974178 |
Download Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine Book in PDF, ePub and Kindle
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 | : Thomas W. O'Gorman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 365 |
| Release | : 2012-03-13 |
| Genre | : Mathematics |
| ISBN | : 0470922257 |
Download Adaptive Tests of Significance Using Permutations of Residuals with R and SAS Book in PDF, ePub and Kindle
Provides the tools needed to successfully perform adaptive tests across a broad range of datasets Adaptive Tests of Significance Using Permutations of Residuals with R and SAS illustrates the power of adaptive tests and showcases their ability to adjust the testing method to suit a particular set of data. The book utilizes state-of-the-art software to demonstrate the practicality and benefits for data analysis in various fields of study. Beginning with an introduction, the book moves on to explore the underlying concepts of adaptive tests, including: Smoothing methods and normalizing transformations Permutation tests with linear methods Applications of adaptive tests Multicenter and cross-over trials Analysis of repeated measures data Adaptive confidence intervals and estimates Throughout the book, numerous figures illustrate the key differences among traditional tests, nonparametric tests, and adaptive tests. R and SAS software packages are used to perform the discussed techniques, and the accompanying datasets are available on the book's related website. In addition, exercises at the end of most chapters enable readers to analyze the presented datasets by putting new concepts into practice. Adaptive Tests of Significance Using Permutations of Residuals with R and SAS is an insightful reference for professionals and researchers working with statistical methods across a variety of fields including the biosciences, pharmacology, and business. The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.
| Author | : Dmitry G. Arseniev |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 290 |
| Release | : 2018-01-09 |
| Genre | : Mathematics |
| ISBN | : 3110554631 |
Download Adaptive Stochastic Methods Book in PDF, ePub and Kindle
This monograph develops adaptive stochastic methods in computational mathematics. The authors discuss the basic ideas of the algorithms and ways to analyze their properties and efficiency. Methods of evaluation of multidimensional integrals and solutions of integral equations are illustrated by multiple examples from mechanics, theory of elasticity, heat conduction and fluid dynamics. Contents Part I: Evaluation of Integrals Fundamentals of the Monte Carlo Method to Evaluate Definite Integrals Sequential Monte Carlo Method and Adaptive Integration Methods of Adaptive Integration Based on Piecewise Approximation Methods of Adaptive Integration Based on Global Approximation Numerical Experiments Adaptive Importance Sampling Method Based on Piecewise Constant Approximation Part II: Solution of Integral Equations Semi-Statistical Method of Solving Integral Equations Numerically Problem of Vibration Conductivity Problem on Ideal-Fluid Flow Around an Airfoil First Basic Problem of Elasticity Theory Second Basic Problem of Elasticity Theory Projectional and Statistical Method of Solving Integral Equations Numerically
| Author | : Bernhard Schipp |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 438 |
| Release | : 2008-11-27 |
| Genre | : Mathematics |
| ISBN | : 3790821217 |
Download Statistical Inference, Econometric Analysis and Matrix Algebra Book in PDF, ePub and Kindle
This Festschrift is dedicated to Götz Trenkler on the occasion of his 65th birthday. As can be seen from the long list of contributions, Götz has had and still has an enormous range of interests, and colleagues to share these interests with. He is a leading expert in linear models with a particular focus on matrix algebra in its relation to statistics. He has published in almost all major statistics and matrix theory journals. His research activities also include other areas (like nonparametrics, statistics and sports, combination of forecasts and magic squares, just to mention afew). Götz Trenkler was born in Dresden in 1943. After his school years in East G- many and West-Berlin, he obtained a Diploma in Mathematics from Free University of Berlin (1970), where he also discovered his interest in Mathematical Statistics. In 1973, he completed his Ph.D. with a thesis titled: On a distance-generating fu- tion of probability measures. He then moved on to the University of Hannover to become Lecturer and to write a habilitation-thesis (submitted 1979) on alternatives to the Ordinary Least Squares estimator in the Linear Regression Model, a topic that would become his predominant ?eld of research in the years to come.
