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Estimands Estimators and Sensitivity Analysis in Clinical Trials

Estimands Estimators and Sensitivity Analysis in Clinical Trials

The concepts of estimands analyses (estimators) and sensitivity are interrelated. Therefore great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language providing technical details real-world examples and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface such as between causal inference and clinical trialists as well as among clinicians statisticians and regulators when it comes to communicating decision-making objectives assumptions and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers¿ A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges¿ A thorough treatment of intercurrent events (ICEs) i. e. postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)¿ Details on how estimands integrated into a principled study development process lay a foundation for coherent specification of trial design conduct and analysis needed to overcome the issues caused by ICEs:¿ A perspective on the role of the intention-to-treat principle¿ Examples and case studies from various areas¿ Example code in SAS and R¿ A connection with causal inference¿ Implications and methods for analysis of longitudinal trials with missing dataTogether the authors have offered the readers their ample expertise in clinical trial design and analysis from an industrial and academic perspective. | Estimands Estimators and Sensitivity Analysis in Clinical Trials

GBP 39.99
1

The BUGS Book A Practical Introduction to Bayesian Analysis

The BUGS Book A Practical Introduction to Bayesian Analysis

Bayesian statistical methods have become widely used for data analysis and modelling in recent years and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS including prediction missing data model criticism and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models techniques for criticism and comparison and a wide range of modelling issues before going into the vital area of hierarchical models one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism model comparison sensitivity analysis to alternative priors and thoughtful choice of prior distributions all those aspects of the art of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological the authors systematically work through the large range of tricks that reveal the real power of the BUGS software for example dealing with missing data censoring grouped data prediction ranking parameter constraints and so on. Many of the examples are biostatistical but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples exercises and some solutions can be found on the book‘s website. | The BUGS Book A Practical Introduction to Bayesian Analysis

GBP 180.00
1

Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data survey data cohort studies and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

GBP 90.00
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Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

This book introduces best practices in longitudinal data analysis at intermediate level with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological health and social and behavioral sciences working with longitudinal studies without a mathematical background. | Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

GBP 89.99
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Design and Analysis of Pragmatic Trials

Design and Analysis of Pragmatic Trials

This book begins with an introduction of pragmatic cluster randomized trials (PCTs) and reviews various pragmatic issues that need to be addressed by statisticians at the design stage. It discusses the advantages and disadvantages of each type of PCT and provides sample size formulas sensitivity analyses and examples for sample size calculation. The generalized estimating equation (GEE) method will be employed to derive sample size formulas for various types of outcomes from the exponential family including continuous binary and count variables. Experimental designs that have been frequently employed in PCTs will be discussed including cluster randomized designs matched-pair cluster randomized design stratified cluster randomized design stepped-wedge cluster randomized design longitudinal cluster randomized design and crossover cluster randomized design. It demonstrates that the GEE approach is flexible to accommodate pragmatic issues such as hierarchical correlation structures different missing data patterns randomly varying cluster sizes etc. It has been reported that the GEE approach leads to under-estimated variance with limited numbers of clusters. The remedy for this limitation is investigated for the design of PCTs. This book can assist practitioners in the design of PCTs by providing a description of the advantages and disadvantages of various PCTs and sample size formulas that address various pragmatic issues facilitating the proper implementation of PCTs to improve health care. It can also serve as a textbook for biostatistics students at the graduate level to enhance their knowledge or skill in clinical trial design. Key Features: Discuss the advantages and disadvantages of each type of PCTs and provide sample size formulas sensitivity analyses and examples. Address an unmet need for guidance books on sample size calculations for PCTs; A wide variety of experimental designs adopted by PCTs are covered; The sample size solutions can be readily implemented due to the accommodation of common pragmatic issues encountered in real-world practice; Useful to both academic and industrial biostatisticians involved in clinical trial design; Can be used as a textbook for graduate students majoring in statistics and biostatistics. | Design and Analysis of Pragmatic Trials

GBP 89.99
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Design and Analysis of Bridging Studies

Design and Analysis of Bridging Studies

As the development of medicines has become more globalized the geographic variations in the efficacy and safety of pharmaceutical products need to be addressed. To accelerate the product development process and shorten approval time researchers are beginning to design multiregional trials that incorporate subjects from many countries around the world under the same protocol. Design and Analysis of Bridging Studies addresses the issues arising from bridging studies and multiregional clinical trials. For bridging studies the book explores ethnic sensitivity the necessity of bridging studies types of bridging studies and the assessment of similarity between regions based on bridging evidence. For multiregional clinical trials the text considers regional differences assesses the consistency of treatment effect across regions and discusses sample size determination for each region. Taking into account the International Conference Harmonisation (ICH) E5 framework for bridging studies the book provides a unified summary of the growing literature and research activities in this area. It covers the regulatory requirements scientific and practical issues and statistical methodology for designing and evaluating bridging studies and multiregional clinical trials with the goal of inspiring new research activities in the field.

GBP 44.99
1

Robust Statistical Methods with R Second Edition

Robust Statistical Methods with R Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features• Provides a systematic practical treatment of robust statistical methods• Offers a rigorous treatment of the whole range of robust methods including the sequential versions of estimators their moment convergence and compares their asymptotic and finite-sample behavior• The extended account of multivariate models includes the admissibility shrinkage effects and unbiasedness of two-sample tests• Illustrates the small sensitivity of the rank procedures in the measurement error model• Emphasizes the computational aspects supplies many examples and illustrations and provides the own procedures of the authors in the R software on the book’s website | Robust Statistical Methods with R Second Edition

GBP 39.99
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Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

Surrogates: a graduate textbook or professional handbook on topics at the interface between machine learning spatial statistics computer simulation meta-modeling (i. e. emulation) design of experiments and optimization. Experimentation through simulation human out-of-the-loop statistical support (focusing on the science) management of dynamic processes online and real-time analysis automation and practical application are at the forefront. Topics include:Gaussian process (GP) regression for flexible nonparametric and nonlinear modeling. Applications to uncertainty quantification sensitivity analysis calibration of computer models to field data sequential design/active learning and (blackbox/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning local GP approximation modeling of simulation experiments (e. g. agent-based models) with coupled nonlinear mean and variance (heteroskedastic) models. Treatment appreciates historical response surface methodology (RSM) and canonical examples but emphasizes contemporary methods and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour complete with motivation from application to and illustration with compelling real-data examples. Presentation targets numerically competent practitioners in engineering physical and biological sciences. Writing is statistical in form but the subjects are not about statistics. Rather they’re about prediction and synthesis under uncertainty; about visualization and information design and decision making computing and clean code. | Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

GBP 38.99
1

Classification of Lipschitz Mappings

Classification of Lipschitz Mappings

Classification of Lipschitz Mappings presents a systematic self-contained treatment of a new classification of Lipschitz mappings and its application in many topics of metric fixed point theory. Suitable for readers interested in metric fixed point theory differential equations and dynamical systems the book only requires a basic background in functional analysis and topology. The author focuses on a more precise classification of Lipschitzian mappings. The mean Lipschitz condition introduced by Goebel Japón Pineda and Sims is relatively easy to check and turns out to satisfy several principles: Regulating the possible growth of the sequence of Lipschitz constants k(Tn)Ensuring good estimates for k0(T) and k∞(T)Providing some new results in metric fixed point theory

GBP 59.99
1

Handbook of Missing Data Methodology

Handbook of Missing Data Methodology

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing including likelihood and Bayesian methods; semi-parametric methods with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative routinely non-verifiable assumptions about the missing data process. The final part discusses special topics such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

GBP 56.99
1

Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

Helps Students Understand Mathematical Programming Principles and Solve Real-World ApplicationsSupplies enough mathematical rigor yet accessible enough for undergraduatesIntegrating a hands-on learning approach a strong linear algebra focus Maple™ software and real-world applications Linear and Nonlinear Programming with Maple™: An Interactive Applications-Based Approach introduces undergraduate students to the mathematical concepts and principles underlying linear and nonlinear programming. This text fills the gap between management science books lacking mathematical detail and rigor and graduate-level books on mathematical programming. Essential linear algebra toolsThroughout the text topics from a first linear algebra course such as the invertible matrix theorem linear independence transpose properties and eigenvalues play a prominent role in the discussion. The book emphasizes partitioned matrices and uses them to describe the simplex algorithm in terms of matrix multiplication. This perspective leads to streamlined approaches for constructing the revised simplex method developing duality theory and approaching the process of sensitivity analysis. The book also discusses some intermediate linear algebra topics including the spectral theorem and matrix norms. Maple enhances conceptual understanding and helps tackle problemsAssuming no prior experience with Maple the author provides a sufficient amount of instruction for students unfamiliar with the software. He also includes a summary of Maple commands as well as Maple worksheets in the text and online. By using Maple’s symbolic computing components numeric capabilities graphical versatility and intuitive programming structures students will acquire a deep conceptual understanding of major mathematical programming principles along with the ability to solve moderately sized rea | Linear and Nonlinear Programming with Maple An Interactive Applications-Based Approach

GBP 59.99
1

Bayesian Statistical Methods

Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods the book covers many general topics: Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regression Handling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code motivating data sets and complete data analyses are available on the book’s website. Brian J. Reich Associate Professor of Statistics at North Carolina State University is currently the editor-in-chief of the Journal of Agricultural Biological and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh Professor of Statistics at North Carolina State University has over 22 years of research and teaching experience in conducting Bayesian analyses received the Cavell Brownie mentoring award and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

GBP 39.99
1

Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

Handbook of Approximation Algorithms and Metaheuristics Second Edition reflects the tremendous growth in the field over the past two decades. Through contributions from leading experts this handbook provides a comprehensive introduction to the underlying theory and methodologies as well as the various applications of approximation algorithms and metaheuristics. Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction relaxation local ratio approximation schemes randomization tabu search evolutionary computation local search neural networks and other metaheuristics. It also explores multi-objective optimization reoptimization sensitivity analysis and stability. Traditional applications covered include: bin packing multi-dimensional packing Steiner trees traveling salesperson scheduling and related problems. Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization computational geometry and graphs problems as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering networks (sensor and wireless) communication bioinformatics search streams virtual communities and more. About the EditorTeofilo F. Gonzalez is a professor emeritus of computer science at the University of California Santa Barbara. He completed his Ph. D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma the Pennsylvania State University and the University of Texas at Dallas before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling graph algorithms computational geometry message communication wire routing etc. | Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

GBP 44.99
1

Temporal Data Mining

Temporal Data Mining

Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today. From basic data mining concepts to state-of-the-art advances Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation similarity computation data classification clustering pattern discovery and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics business and industrial applications web usage mining and spatiotemporal data mining. Along with various state-of-the-art algorithms each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa. wordpress. com/

GBP 59.99
1

Clean Numerical Simulation

Clean Numerical Simulation

This book describes the basic principles of Clean Numerical Simulation (CNS) proposed by the author in 2009 as well as several of its applications. Unlike conventional algorithms CNS gives in a sufficiently long interval of time a convergent chaotic trajectory whose numerical noise is much lower than the true physical solution so that one can gain accurately. Thus CNS provides for the first time an ability to check statistics stability of chaos leading to a completely new concept of ultra-chaos which has both trajectory instability and statistics instability and thus is of a higher disorder. Notably it is impossible to repeat experimental results of ultra-chaos even in the statistical sense. However the reproducibility of physical experiments forms a cornerstone for modern science. Thus ultra-chaos reveals an incompleteness of the modern science paradigm. In addition it also reveals statistics stability as a precondition for use of conventional algorithms including direct numerical simulation (DNS). In Clean Numerical Simulation several conjectures and open problems are proposed including a modified fourth Clay millennium problem. Indeed CNS opens the door for us to enter the clean numerical world of chaos and turbulence.

GBP 74.99
1

Design and Analysis of Experiments Classical and Regression Approaches with SAS

Design and Analysis of Experiments Classical and Regression Approaches with SAS

Unlike other books on the modeling and analysis of experimental data Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory it also explores regression approaches. Capitalizing on the availability of cutting-edge software the author uses both manual methods and SAS programs to carry out analyses. The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation the completely randomized design the use of orthogonal contrast to test hypotheses and the model adequacy check. With an emphasis on two-factor factorial experiments the author analyzes repeated measures as well as fixed random and mixed effects models. He also describes designs with randomization restrictions before delving into the special cases of the 2k and 3k factorial designs including fractional replication and confounding. In addition the book covers response surfaces balanced incomplete block and hierarchical designs ANOVA ANCOVA and MANOVA. Fortifying the theory and computations with practical exercises and supplemental material this distinctive text provides a modern comprehensive treatment of experimental design and analysis. | Design and Analysis of Experiments Classical and Regression Approaches with SAS

GBP 59.99
1

Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.

GBP 82.99
1

Introduction to Python Programming

Introduction to Python Programming

Introduction to Python Programming is written for students who are beginners in the field of computer programming. This book presents an intuitive approach to the concepts of Python Programming for students. This book differs from traditional texts not only in its philosophy but also in its overall focus level of activities development of topics and attention to programming details. The contents of the book are chosen with utmost care after analyzing the syllabus for Python course prescribed by various top universities in USA Europe and Asia. Since the prerequisite know-how varies significantly from student to student the book’s overall overture addresses the challenges of teaching and learning of students which is fine-tuned by the authors’ experience with large sections of students. This book uses natural language expressions instead of the traditional shortened words of the programming world. This book has been written with the goal to provide students with a textbook that can be easily understood and to make a connection between what students are learning and how they may apply that knowledge. Features of this book This book does not assume any previous programming experience although of course any exposure to other programming languages is useful This book introduces all of the key concepts of Python programming language with helpful illustrations Programming examples are presented in a clear and consistent manner Each line of code is numbered and explained in detail Use of f-strings throughout the book Hundreds of real-world examples are included and they come from fields such as entertainment sports music and environmental studies Students can periodically check their progress with in-chapter quizzes that appear in all chapters

GBP 160.00
1

Statistical Theory A Concise Introduction

Statistical Theory A Concise Introduction

Designed for a one-semester advanced undergraduate or graduate statistical theory course Statistical Theory: A Concise Introduction Second Edition clearly explains the underlying ideas mathematics and principles of major statistical concepts including parameter estimation confidence intervals hypothesis testing asymptotic analysis Bayesian inference linear models nonparametric statistics and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions theorems and proofs. Based on the authors’ lecture notes the book is self-contained which maintains a proper balance between the clarity and rigor of exposition. In a few cases the authors present a sketched version of a proof explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments. Features: Second edition has been updated with a new chapter on Nonparametric Estimation; a significant update to the chapter on Statistical Decision Theory; and other updates throughout No requirement for heavy calculus and simple questions throughout the text help students check their understanding of the material Each chapter also includes a set of exercises that range in level of difficulty Self-contained and can be used by the students to understand the theory Chapters and sections marked by asterisks contain more advanced topics and may be omitted Special chapters on linear models and nonparametric statistics show how the main theoretical concepts can be applied to well-known and frequently used statistical tools The primary audience for the book is students who want to understand the theoretical basis of mathematical statistics—either advanced undergraduate or graduate students. It will also be an excellent reference for researchers from statistics and other quantitative disciplines. | Statistical Theory A Concise Introduction

GBP 74.99
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