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A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout.
Aimed at applied researchers and graduate students, this text provides a comprehensive overview of the framework of random effects joint models. Emphasis is given on applications such that readers will obtain a clear view on the type of research questions that are best answered using a joint modeling approach, the basic features of these models, and how they can be extended in practice.
Special mention is given in checking the assumptions using residual plots, and on dynamic predictions for the survival and longitudinal outcomes. The R Student Companion. The R Student Companion is a student-oriented manual describing how to use R in high school and college science and mathematics courses.
Written for beginners in scientific computation, the book assumes the reader has just some high school algebra and has no computer programming background. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R in an appendix. In addition, each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups.
Several of the chapters explore algebra concepts that are highly useful in scientific applications, such as quadratic equations, systems of linear equations, trigonometric functions, and exponential functions. Each chapter provides an instructional review of the algebra concept, followed by a hands-on guide to performing calculations and graphing in R. R is intuitive, even fun. Fantastic, publication-quality graphs of data, equations, or both can be produced with little effort. By integrating mathematical computation and scientific illustration early in a student's development, R use can enhance one's understanding of even the most difficult scientific concepts.
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While R has gained a strong reputation as a package for statistical analysis, The R Student Companion approaches R more completely as a comprehensive tool for scientific computing and graphing. R for Statistics. Published in , the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.
Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.
Shipunov, E. Baldin, P. Volkova, A. Korobejnikov, S. Nazarova, S. Petrov, and V. Nagljadnaja statistika. Ispoljzuem R! DMK Press, Moscow, It is intended to help people who begin to learn statistical methods. All explanations are based on R. The book may also serve as an introduction reference to R. Springer, Collection Pratique R, 1st edition, Les commandes permettant le traitement des exemples sous le logiciel R figurent dans le corps du texte.
Chapman & Hall/CRC Biostatistics Series
R Cookbook. O'Reilly, first edition, Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master. R Cookbook will help both beginners and experienced statistical programmers unlock and use the power of R. O'Reilly, Each solution gives you just what you need to know to get started with R for basic statistics, graphics, and regression. These solutions were selected from O'Reilly's R Cookbook, which contains more than recipes for R.
R Graphics, Second Edition. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graphics. The book reviews the graphics facilities of the R language and describes R's powerful grid graphics system.
It then covers the graphics engine, which represents a common set of fundamental graphics facilities, and provides a series of brief overviews of the major areas of application for R graphics and the major extensions of R graphics. Mathematical Statistics with Resampling and R. Wiley, 1st edition, This book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.
The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis, Calculation of sampling distributions, The Central Limit Theorem, Monte Carlo sampling, Maximum likelihood estimation and properties of estimators, Confidence intervals and hypothesis tests, Regression, Bayesian methods.
Case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the material. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels.
It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work. An R Companion to Applied Regression. It introduces R, and concentrates on how to use linear and generalized-linear models in R while assuming familiarity with the statistical methodology.
R Graphs Cookbook. Packt Publishing, This practical guide begins by teaching you how to make basic graphs in R and progresses through subsequent dedicated chapters about each graph type in depth. It will demystify a lot of difficult and confusing R functions and parameters and enable you to construct and modify data graphics to suit your analysis, presentation, and publication needs.
Data Mining with Rattle and R: The art of excavating data for knowledge discovery. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining.
With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet.
Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. Numerical Methods and Optimization in Finance. Academic Press, It covers fundamental numerical analysis and computational techniques, for example for option pricing, but two topics are given special attention: simulation and optimization. Many chapters are organized as case studies, dealing with problems like portfolio insurance or risk estimation; in particular, several chapters explain optimization heuristics and how to use them for portfolio selection or the calibration of option pricing models.
Such practical examples allow readers to learn the required steps for solving specific problems, and to apply these steps to other problems, too. At the same time, the chosen applications are relevant enough to make the book a useful reference on how to handle given problems. Analysis of Questionnaire Data with R. As with written material, there are almost always differences between the meaning of the original text and translated text.
Additionally, many versions can be suggested, each with their advantages and disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations. As indicated in the title, the syntax of these statistical formulations is based on the well-known R language, chosen for its popularity, simplicity, and power of its structure.
Although syntax is vital, understanding the semantics is the real challenge of any good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand.
Sometimes data sets contain errors, inconsistencies between answers, or missing data.
More often, available statistical tools are not formally appropriate for the given situation, making it difficult to assess to what extent this slight inadequacy affects the interpretation of results. Analysis of Questionnaire Data with R tackles these and other common challenges in the practice of statistics. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation.
Later chapters introduce abstract data structures ADTs and parallel computing concepts. The translation of a mathematical problem into its computational analog or analogs is a skill that must be learned, like any other, by actively solving relevant problems. The book lays the foundation for original code development in a research environment. The R Primer. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software.
Rather than explore the many options available for every command as well as the ever-increasing number of packages, the book focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point. The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production.
Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated. While base R is used throughout, other functions or packages are listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R.
Additional topics and R code are available from the book's supporting website at www. Robert and George Casella. Springer, 1st edition, Ce livre adopte donc le point de vue du programmeur pour exposer ces outils fondamentaux de simulation stochastique. Tous les chapitres incluent des exercices. R Companion to Linear Models. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.
This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.
The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models MGLMMs using either standard or adaptive Gaussian quadrature.
The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters.
Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on.
As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided the freely available programming language R is used throughout.
Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics.
Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US. R for Stata Users. Statistics and Computing. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches.
When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. R in Action. Manning, The book begins by introducing the R language, including the development environment. As you work through various examples illustrating R's features, you'll also get a crash course in practical statistics, including basic and advanced models for normal and non- normal data, longitudinal and survival data, and a wide variety of multivariate methods. Both data mining methodologies and approaches to messy and incomplete data are included.
Statistiques avec R. Didact Statistiques. Presses Universitaires de Rennes, 2nd edition, Le Logiciel R. R in a Nutshell [deutsche Ausgabe]. Along the way, you will learn how to use R to arrive at practical solutions and how to effectively communicate your results. Ultimately, the fate of the kingdom depends on your ability to make informed, data- driven decisions with R. Computer Sciences and Data Analysis.
It covers principal component analysis PCA when variables are quantitative, correspondence analysis CA and multiple correspondence analysis MCA when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables.
Within this framework, they present the principles, indicators, and ways of representing and visualizing objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical.
They also illustrate the methods and the ways they can be exploited using examples from various fields. Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data. Statistics and Data Analysis for Financial Engineering. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors.
Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration.
The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful. Introducing Monte Carlo Methods with R. Use R. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.
While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis-Hastings and Gibbs algorithms, and adaptive algorithms.
All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more.
About the Series
The programming parts are introduced progressively to be accessible to any reader. Clinical Trial Data Analysis with R. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book's practical, detailed approach draws on the authors' 30 years of real-world experience in biostatistical research and clinical development.
Each chapter presents examples of clinical trials based on the authors' actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data. With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.
Spatial Statistics and Modeling. Springer Series in Statistics. This book covers the best-known spatial models for three types of spatial data: geostatistical data stationarity, intrinsic models, variograms, spatial regression and space-time models , areal data Gibbs-Markov fields and spatial auto-regression and point pattern data Poisson, Cox, Gibbs and Markov point processes. The level is relatively advanced, and the presentation concise but complete.
The most important statistical methods and their asymptotic properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models. A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises. While a foundation in probability and mathematical statistics is assumed, three appendices introduce some necessary background.
The book is accessible to senior undergraduate students with a solid math background and Ph. Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference. Forest Analytics with R.
The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling.
The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management.
The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Vinod, editor. Lecture Notes in Statistics. The following twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software.
McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime. Koenker: Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition.
Gelman: R graphics in the context of voter participation in US elections. Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment. Markus and Gu: New R tools for exploratory data analysis including bubble plots. Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts, relevant to anyone with an investment portfolio.
Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment. Haupt, Schnurbus, and Tschernig: consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation. Rindskopf: R methods to fit a multinomial based multivariate analysis of variance ANOVA with examples from psychology, sociology, political science, and medicine.
Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. Numatsi and Rengifo: explain persistent discrete jumps in financial series subject to misspecification. It introduces tools to enable readers to learn and use fundamental methods for constructing quantitative models of biological mechanisms, both deterministic and with some elements of randomness, including complex reaction equilibria and kinetics, population models, and regulation of metabolism and development; to understand how concepts of probability can help in explaining important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data from spectroscopic, genomic, and proteomic sources.
These quantitative tools are implemented using the free, open source software program R. R provides an excellent environment for general numerical and statistical computing and graphics, with capabilities similar to Matlab.
Statistical Analysis of Human Growth and Development - Yin Bun Cheung - Google книги
Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers [ 76 ] Uwe Ligges. Programmieren mit R. Springer-Verlag, Heidelberg, 3rd edition, Generalised Linear Models in R]. Biologie dnes. Scientia, Praha, Je urcena predevsim studentum a kolegum z biologickych oboru a vyzaduje pouze zakladni statisticke vzdelani, jakym je napr.
Text knihy obsahuje nezbytne minimum statisticke teorie, predevsim vsak reseni 18 realnych prikladu z oblasti biologie. Kazdy priklad je rozpracovan od popisu a stanoveni cile pres vyvoj statistickeho modelu az po zaver. K analyze dat je pouzit popularni a volne dostupny statisticky software R. Priklady byly zamerne vybrany tak, aby upozornily na lecktere problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout. Zaroven maji ctenare motivovat k tomu, jak o statistickych modelech premyslet a jak je pouzivat.
Reseni prikladu si muse ctenar vyzkouset sam na datech, jez jsou dodavana spolu s knihou. Springer Series in Statistics and Computing. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download.
Heiberger and Erich Neuwirth. R Through Excel. The presentation is designed as a computational supplement to introductory statistics texts. The authors provide RExcel examples for most topics in the introductory course. Data can be transferred from Excel to R and back. The clickable RExcel menu supplements the powerful R command language. Results from the analyses in R can be returned to the spreadsheet.
Ordinary formulas in spreadsheet cells can use functions written in R. The book is accessible to readers with only a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. R code is provided throughout the text. Cowpertwait and Andrew Metcalfe. Introductory Time Series with R. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the R routine for fitting it to the data.
Finally, the model is applied to an analysis of a historical data set. By using R, the whole procedure can be reproduced by the reader. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyze time series as part of their taught program or their research. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.
A Primer of Ecology with R. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will all find this to be a useful overview of important topics. Introduction to Multivariate Statistical Analysis in Chemometrics.
It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects both the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts.
In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts.
It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data. Broman and Saunak Sen. Two moderately challenging case studies illustrate QTL analysis in its entirety. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research. Wiley-VCH, Requiring only little mathematical prerequisite in calculus and linear algebra, it is accessible to scientists, engineers, and students at the undergraduate level.
Bayesian Computation with R. Springer, 2nd edition, The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling.
Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner's g priors to choose between models in linear regression.
There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package. Ramsay, Giles Hooker, and Spencer Graves. Functional Data Analysis with R and Matlab.
Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase the functional data analysis. This book presents methods for the randomized 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. The authors compare various designs of clinical trials, include real examples of clinical trials, and present simulations of many procedures for small to moderate-sized samples. They also use results from the optimum design of experiments to create powerful and flexible adaptive designs.
Adaptive experimental designs are sequential procedures for collecting data. In each step, the observer can use all of the information gathered to determine whether to stop or carry on with the study. This book focuses on recently developed methods in adaptive randomization for treatment allocation. It illustrates designs that take into account past treatment allocations only, discusses procedures that use past data, and presents compromise strategies that deal with ethical issues.