Basic Biostatistics is a concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. The text puts equal emphasis on exploratory and confirmatory statistical methods. Sampling, exploratory data analysis, estimation, hypothesis testing, and power and precision are covered through detailed, illustrative examples.The book is organized into three parts: Part I addresses basic concepts and techniques; Part II covers analytic techniques for quantitative response variables; and Part III covers techniques for categorical responses.
Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences. The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors. The book is based on the author's 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields. Readers will find hyperlinks to free online tests to help novice statisticians perform their own statistical tests. Discusses how to use the standard statistical tests in the biomedical field, as well as how to make statistical inferences (t test, ANOVA, regression etc.) Includes non-standards tests, including equivalence or non-inferiority testing, extreme value statistics, cross-over tests, and simple time series procedures such as the runs test and Cusums Introduces procedures such as multiple regression, Poisson regression, meta-analysis and resampling statistics, and provides references for further studies Presents a vast array of free on-line statistical testing tools which allow readers to practice the procedures learned
The Concise Encyclopedia of Statistics presents the essential information about statistical tests, concepts, and analytical methods in language that is accessible to practitioners and students of the vast community using statistics in medicine, engineering, physical science, life science, social science, and business/economics. The reference is alphabetically arranged to provide quick access to the fundamental tools of statistical methodology and biographies of famous statisticians. The more than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings.
This dictionary contains over 2,000 entries on all aspects of statistics, including terms used in computing, mathematics, and probability, as well as biographical information on over 200 key figures in the field, and coverage of statistical journals and societies. The entries are generously illustrated with useful figures and diagrams, and include worked examples where applicable.
The Encyclopedia of Statistical Sciences is a superior and unique source of information on statistical theory, methods, and applications. This new edition is designed to bring the encyclopedia in line with the latest topics and advances made in statistical science over the past decade--in areas such as computer-intensive statistical methodology, genetics, medicine, the environment, and other applications. The entries are self-contained and easily understood by readers with a limited statistical background.
This book reflects the increasing awareness of environmental influences on health and how to assess and measure them. It covers the main methodological issues in environmental epidemiology including: how to assess and critically interpret scientific data relating to potential environmental hazards to health and much more.
The Handbook of Probability presents the fundamentals of probability with an emphasis on the balance of theory, application,and methodology. Utilizing basic examples throughout, the handbook expertly transitions between concepts and practice to allow readers an inclusive introduction to the field of probability. The Handbook of Probability offers coverage of: Probability Space Probability Measure Random Variables Random Vectors in Rn Characteristic Function Moment Generating Function Gaussian Random Vectors Convergence Types Limit Theorems.
Handbook of Spatial Epidemiologyexplains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering. Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.
This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.
This book provides an overview of demographic analysis and methods. Concepts and methods, from the nature of demographic information through data collection and the basics of statistical measures and on to demographic analysis itself are succinctly explained. Measures and analyses of fertility, mortality, life tables, migration and demographic events such as marriage, education and labour force are described while later chapters cover multiple decrement tables, population projections, the importance of testing and smoothing demographic data, the stable population model and demographic software. An emphasis on practical aspects and the use of real-life examples based on data from around the globe make this book accessible.
This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models.
BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
This book offers practical information about the methodology of epidemiologic studies of obesity. The book is structured in four main sections. The first section considers issues surrounding the definition of obesity, measurement techniques, and the designs of epidemiologic studies. The second section addresses the consequences of obesity, looking at epidemiologic studies that focus on cardio-vascular disease, diabetes, and cancer. The third section looks at determinants obesity, reviewing a wide range of risk factors for obesity including diet, physical activity and sedentary behaviors, sleep disorders, psychosocial factors, physical environment, biochemical and genetic predictors, and intrauterine exposures. The final section addresses the analytical issues and challenges for epidemiologic studies of obesity.
The new edition of this critically praised textbook continues to provide the most comprehensive overview of the concepts, methods, and research advances in the field; particularly the application of molecular genomics and of neuroimaging. It has been revised and enhanced to capitalize on the strengths of the first and second editions while keeping it up-to-date with the field of psychiatry and epidemiology. This comprehensive publication now includes chapters on experimental epidemiology, gene-environment interactions, the use of case registries, eating disorders, suicide, childhood disorders and immigrant populations, and the epidemiology of a number of childhood disorders.