Robust Methods in Biostatistics

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests. This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way. Key features: Discusses confidence intervals and p-values in terms of confidence functions. Explains basic statistical methodology represented in terms of graphics rather than mathematical formulae, whilst highlighting the mathematical basis of biostatistics. Looks at problems of estimating parameters in statistical models and looks at the similarities between different models. Provides an extensive discussion on the position of statistics within the medical scientific process. Discusses distribution functions, including the Guassian distribution and its importance in biostatistics. This book will be useful for biostatisticians with little mathematical background as well as those who want to understand the connections in biostatistics and mathematical issues.

As rapid advances in biotechnology occur, there is a need for a pedagogical tool to aid current students and laboratory professionals in biotechnological methods; Methods in Biotechnology is an invaluable resource for those students and professionals. Methods in Biotechnology engages the reader by implementing an active learning approach, provided advanced study questions, as well as pre- and post-lab questions for each lab protocol. These self-directed study sections encourage the reader to not just perform experiments but to engage with the material on a higher level, utilizing critical thinking and troubleshooting skills. This text is broken into three sections based on level – Methods in Biotechnology, Advanced Methods in Biotechnology I, and Advanced Methods in Biotechnology II. Each section contains 14-22 lab exercises, with instructor notes in appendices as well as an answer guide as a part of the book companion site. This text will be an excellent resource for both students and laboratory professionals in the biotechnology field.

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials. Key Features: Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques. Contains introductory explanations of Bayesian principles common to all areas of application. Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics. Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs. Highlights the differences between the Bayesian and classical approaches. Supported by an accompanying website hosting free software and case study guides. Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.

Maintaining the same accessible and hands-on presentation, Introductory Biostatistics, Second Edition continues to provide an organized introduction to basic statistical concepts commonly applied in research across the health sciences. With plenty of real-world examples, the new edition provides a practical, modern approach to the statistical topics found in the biomedical and public health fields. Beginning with an overview of descriptive statistics in the health sciences, the book delivers topical coverage of probability models, parameter estimation, and hypothesis testing. Subsequently, the book focuses on more advanced topics with coverage of regression analysis, logistic regression, methods for count data, analysis of survival data, and designs for clinical trials. This extensive update of Introductory Biostatistics, Second Edition includes: • A new chapter on the use of higher order Analysis of Variance (ANOVA) in factorial and block designs • A new chapter on testing and inference methods for repeatedly measured outcomes including continuous, binary, and count outcomes • R incorporated throughout along with SAS®, allowing readers to replicate results from presented examples with either software • Multiple additional exercises, with partial solutions available to aid comprehension of crucial concepts • Notes on Computations sections to provide further guidance on the use of software • A related website that hosts the large data sets presented throughout the book Introductory Biostatistics, Second Edition is an excellent textbook for upper-undergraduate and graduate students in introductory biostatistics courses. The book is also an ideal reference for applied statisticians working in the fields of public health, nursing, dentistry, and medicine.

Biostatistics Decoded  covered a large number of statistical methods that are mainly applied to clinical and epidemiological research, as well as a comprehensive discussion of study designs for observational research and clinical trials, two important concerns for the clinical researcher. In this second edition, new material is included covering statistical methods and study designs that are used to analyse research. Following the same methodology used in the first edition, the chapters are presented in two levels of detail, one for the reader who wishes only to understand the rationale behind each statistical method, and one for the reader who wishes to understand the computations Key features include: Extensive coverage of the design and analysis of experiments for basic science research Experimental designs are presented together with the statistical methods The rationale of all forms of ANOVA is explained with simple mathematics A comprehensive presentation of statistical tests for multiple comparisons Calculations for all statistical methods are illustrated with examples and explained step-by-step. This book presents biostatistical concepts and methods in a way that is accessible to anyone, regardless of his or her knowledge of mathematics. The topics selected for this book cover will meet the needs of clinical professionals to readers in basic science research.

Study design and statistical methodology are two important concerns for the clinical researcher. This book sets out to address both issues in a clear and concise manner. The presentation of statistical theory starts from basic concepts, such as the properties of means and variances, the properties of the Normal distribution and the Central Limit Theorem and leads to more advanced topics such as maximum likelihood estimation, inverse variance and stepwise regression as well as, time-to-event, and event-count methods. Furthermore, this book explores sampling methods, study design and statistical methods and is organized according to the areas of application of each of the statistical methods and the corresponding study designs. Illustrations, working examples, computer simulations and geometrical approaches, rather than mathematical expressions and formulae, are used throughout the book to explain every statistical method. Biostatisticians and researchers in the medical and pharmaceutical industry who need guidance on the design and analyis of medical research will find this book useful as well as graduate students of statistics and mathematics with an interest in biostatistics. Biostatistics Decoded: Provides clear explanations of key statistical concepts with a firm emphasis on practical aspects of design and analysis of medical research. Features worked examples to illustrate each statistical method using computer simulations and geometrical approaches, rather than mathematical expressions and formulae. Explores the main types of clinical research studies, such as, descriptive, analytical and experimental studies. Addresses advanced modeling techniques such as interaction analysis and encoding by reference and polynomial regression.

Provides a one-stop resource for engineers learning biostatistics using MATLAB® and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB® for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references. Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS also includes: parallel coverage of classical and Bayesian approaches, where appropriate substantial coverage of Bayesian approaches to statistical inference material that has been classroom-tested in an introductory statistics course in bioengineering over several years exercises at the end of each chapter and an accompanying website with full solutions and hints to some exercises, as well as additional materials and examples Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches.

A thorough, practical reference on the social patterns behind health outcomes Methods in Social Epidemiology provides students and professionals with a comprehensive reference for studying the social distribution and social determinants of health. Covering the theory, models, and methods used to measure and analyze these phenomena, this book serves as both an introduction to the field and a practical manual for data collection and analysis. This new second edition has been updated to reflect the field's tremendous growth in recent years, including advancements in statistical modeling and study designs. New chapters delve into genetic methods, structural cofounding, selection bias, network methods, and more, including new discussion on qualitative data collection with disadvantaged populations. Social epidemiology studies the way society's innumerable social interactions, both past and present, yields different exposures and health outcomes between individuals within populations. This book provides a thorough, detailed overview of the field, with expert guidance toward the real-world methods that fuel the latest advances. Identify, measure, and track health patterns in the population Discover how poverty, race, and socioeconomic factors become risk factors for disease Learn qualitative data collection techniques and methods of statistical analysis Examine up-to-date models, theory, and frameworks in the social epidemiology sphere As the field continues to evolve, researchers continue to identify new disease-specific risk factors and learn more about how the social system promotes and maintains well-known exposure disparities. New technology in data science and genomics allows for more rigorous investigation and analysis, while the general thinking in the field has become more targeted and attentive to causal inference and core assumptions behind effect identification. It's an exciting time to be a part of the field, and Methods in Social Epidemiology provides a solid reference for any student, researcher, or faculty in public health.

Balanced coverage of the methodology and theory of numerical methods in finance Numerical Methods in Finance bridges the gap between financial theory and computational practice while helping students and practitioners exploit MATLAB for financial applications. Paolo Brandimarte covers the basics of finance and numerical analysis and provides background material that suits the needs of students from both financial engineering and economics perspectives. Classical numerical analysis methods; optimization, including less familiar topics such as stochastic and integer programming; simulation, including low discrepancy sequences; and partial differential equations are covered in detail. Extensive illustrative examples of the application of all of these methodologies are also provided. The text is primarily focused on MATLAB-based application, but also includes descriptions of other readily available toolboxes that are relevant to finance. Helpful appendices on the basics of MATLAB and probability theory round out this balanced coverage. Accessible for students-yet still a useful reference for practitioners-Numerical Methods in Finance offers an expert introduction to powerful tools in finance.