Product Description: Statistical Questions in Evidence-based Medicine is a book of questions and answers about the statistical principles and methods used in medical research. Based entirely on material published in the medical literature and popular media, it will prove invaluable to medical students, doctors, nurses, medical researchers and others concerned with medical data. This book is a companion volume to the new 3rd edition of An Introduction to Medical Statistics but can also be used in conjunction with the 2nd edition or with other good texts. Short excerpts of material from published papers or summaries of their results are presented with questions. These test and develop the reader's understanding and interpretation of statistics and extend the reader's research and critical appraisal skills, thus encouraging an evidence-based approach. Questions are presented on the left-hand page with detailed answers on the right-hand page. Answers include references to core material in An Introduction to Medical Statistics. The book is intended as a series of examples for self-teaching but could also be read as a series of case studies with detailed commentaries. The questions are clearly graded, using icons, in terms of difficulty and undergraduate or postgraduate level. The book is easy to use and a model of clarity for the reader.
Neighborhoods and Health (Medicine) from: Oxford University Press, USA 10 used from $36.11 19 Thirdparty New from $49.90 Edition: 1 Media: Hardcover EAN: 9780195138382 ISBN: 0195138384 Number Of Pages: 368 Publication Date: 2003-03-20 Publisher: Oxford University Press, USA
Product Description: Do places make a difference to people's health and well-being? The authors of this groundbreaking textbook demonstrate convincingly how the physical and social characteristics of a neighborhood can shape the health of its residents. Drawing on the expertise of a renowned cast of researchers, this book presents a state-of-the art account of the theories, methods, and empirical evidence linking neighborhood conditions to population health. Represented in the volume are contributions from the world's leading investigators in the field, including social epidemiologists, demographers, medical geographers, sociologists, and medical practitioners. This comprehensive textbook lays out for the first time the methodological approaches to conducting neighborhood research, including multi-level and contextual analysis, geocoding and the use of small area-based measures of deprivation, as well as the evolving science of "ecometrics." Substantive chapters present the case for the relevance of neighborhood effects on health outcomes throughout the life cycle, from infant mortality and low birthweight, to childhood asthma, adult infectious diseases, and disability in old age. The approaches covered in the book range from testing the linkages between community-level variables, such as social capital and residential segregation, and population health to designing and implementing community interventions and policies to improve the health of the public. The book is a timely companion volume to Social Epidemiology (Oxford University Press, 2000), edited by the same authors, and an indispensable manual on neighborhood research for students, researchers, and practitioners.
One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data.
Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed.
Product Description: There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
Product Description: While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks") Exercises based on data analyses reinforce concepts
Download Description: While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks") Exercises based on data analyses reinforce concepts
Product Description: Applied statisticians in many fields frequently analyze time-to-event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring and truncation. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data. The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of summary statistics for competing risks probabilities in Chapter 2 and the estimation process for these probabilities in Chapter 4. A new section on tests of the equality of survival curves at a fixed point in time is added in Chapter 7. In Chapter 8 an expanded discussion is presented on how to code covariates and a new section on discretizing a continuous covariate is added. A new section on Lin and Ying's additive hazards regression model is presented in Chapter 10. We now proceed to a general discussion of the usefulness of this book incorporating the new material with that of the first edition.
Product Description: Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.
This book distinguishes itself from other SPSS resources through its unique integration of the research process (including design) and the use and interpretation of the statistics. Designed to help students analyze and interpret research data, the authors demonstrate how to: choose the appropriate statistic based on the research design, interpret SPSS output, and write about the output in a research paper. The authors describe the use and interpretation of these statistics in a user-friendly, non-technical language. The book prepares students for all of the steps in the research process, from design and data collection, to writing about the results.
The new edition features SPSS 14.0 for Windows, but can also be used with older and newer versions. There are also new problems, expanded discussions of effect sizes, and an expanded appendix on getting started with SPSS. The book features discussions of writing about outputs, data entry and checking, reliability assessment, testing assumptions, and descriptive, inferential, and nonparametric statistics. Several related statistics are included in each chapter. SPSS syntax, along with the output, is included for those who prefer this format. Two realistic data sets are available on the book’s CD and are used to solve the end of chapter problems.
SPSS for Introductory Statistics, 3/e provides helpful teaching tools: *all of the key SPSS windows needed to perform the analyses; *complete outputswithcall-out boxes tohighlight key points; *interpretation sections and questions to help students better understand the output;
*lab assignments organized the way students proceed when they conduct a research project; *extra SPSS problems for practice in running and interpreting SPSS; and *helpful appendices on how to: get started with SPSS, write research questions, and create tables and figures.
This book is an ideal supplement for courses in either statistics or research methods taught in departments of psychology, education, and other social and health sciences. The Instructor's Resource CD features PowerPoint slides and answers to and additional information on the questions and problems.
Product Description: 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. With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health.
