Author: Professor of Biostatistics and Epidemiology Steve Selvin
Publisher: Oxford University Press, USA
This book combines applied and theoretical approaches to the analysis of epidemiologic issues. It goes beyond elementary material to deal with real problems generated by disease data, and delves into less usual areas such as the analysis of spatial distributions, survival data, proportional hazards regression, and "computer-intensive" approaches to statistical estimation. Each method discussed in the text is illustrated with examples which include complete sets of data. Using actual data demonstrates the strengths and weaknesses of different analytic approaches in describing a disease process. The goal of the book is to allow the reader to develop a clear understanding of analytic approaches to problems in epidemiologic data analysis without relying on sophisticated mathematics and advanced statistical theory. For the Second Edition a new chapter on the analysis of matched data has been added. This covers both discrete and continuous outcomes and explains both the classic analytic approach and the conditional logistic regression model. New sections have also been added on contingency table data, misclassification, and additive models underlying tabular data. In all the chapters there are new applications and other revisions that make this Second Edition a clearer and more helpful exposition of the way statistical tools are used to analyze epidemiologic data.
Analytic procedures suitable for the study of human disease are scattered throughout the statistical and epidemiologic literature. Explanations of their properties are frequently presented in mathematical and theoretical language. This well-established text gives readers a clear understanding of the statistical methods that are widely used in epidemiologic research without depending on advanced mathematical or statistical theory. By applying these methods to actual data, Selvin reveals the strengths and weaknesses of each analytic approach. He combines techniques from the fields of statistics, biostatistics, demography and epidemiology to present a comprehensive overview that does not require computational details of the statistical techniques described. For the Third Edition, Selvin took out some old material (e.g. the section on rarely used cross-over designs) and added new material (e.g. sections on frequently used contingency table analysis). Throughout the text he enriched existing discussions with new elements, including the analysis of multi-level categorical data and simple, intuitive arguments that exponential survival times cause the hazard function to be constant. He added a dozen new applied examples to illustrate such topics as the pitfalls of proportional mortality data, the analysis of matched pair categorical data, and the age-adjustment of mortality rates based on statistical models. The most important new feature is a chapter on Poisson regression analysis. This essential statistical tool permits the multivariable analysis of rates, probabilities and counts.
Describes and compares a variety of statistical methods for the analysis of biomedical data. Statistical procedures are described by presenting worked examples for each type of procedure. Coverage includes: methods for comparing two groups, methods for evaluating the association between two variables, techniques for epidemiological analysis of 2x2 tables and procedures for estimation and comparison of survival curves. Presents both parametric and non-parametric procedures throughout the text. Additional topics detailed include observer agreement, standardization of rates and methods for analysis of odds ratio.
Environmental epidemiology is the study of the environmental causes of disease in populations and how these risks vary in relation to intensity and duration of exposure and other factors like genetic susceptibility. As such, it is the basic science upon which governmental safety standards and compensation policies for environmental and occupational exposure are based. Profusely illustrated with examples from the epidemiologic literature on ionizing radiation and air pollution, thistext provides a systematic treatment of the statistical challenges that arise in environmental health studies and the use epidemiologic data in formulating public policy, at a level suitable for graduate students and epidemiologic researchers.After a general overview of study design and statistical methods for epidemiology generally, the book goes on to address the problems that are unique to environmental health studies, special-purpose designs like two-phase case-control studies and countermatching, statistical methods for modeling exposure-time-response relationships, longitudinal and time-series studies, spatial and ecologic methods, exposure measurement error, interactions, and mechanistic models. It also discusses studiesaimed at evaluating the public health benefits of interventions to improve the environment, the use of epidemiologic data to establish environmental safety standards and compensation policy, and concludes with emerging problems in reproductive epidemiology, natural and man-made disasters like globalwarming, and the global burden of environmentally caused disease. No other book provides such a broad perspective on the methodological challenges in this field at a level accessible to both epidemiologists and statisticians.
This balanced and well-integrated text gives a lucid overview of the entire process of genetic epidemiology, from familial aggregation through segregation, likage, and association studies. It is illustrated throughout with examples from the literature on cancer genetics. Statistical concepts are developed in depth, but with a focus on applications. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics are included. Oriented to graduate students in biostatistics, epidemiology, and human genetics, the book will also be a useful reference for researchers. It gives equal emphasis to study designs and data analysis.
A single, thorough source of definitions for thousands of statistical terms, illustrated with graphs, charts, tables, and equations. The Pocket Dictionary includes terms used in various fields related to statistics including mathematics, probability, economics, business, decision analysis, demography, epidemiology, bio-statistics, engineering, public health, quality control and many others.
This is the third in a series of books based on articles from the Encyclopedia of Biostatistics. The editors have updated the articles from the Human Genetics section of the EoB, have adpated other articles to give them a genetic feel, and have included a number of newly commissioned articles.
Technology & Engineering by Martti Juhani Karvonen
The present manual tries to respond to the specific needs of occupa- tional health epidemiology. Rather than a comprehensive review of the subject, the book presents a series of articles. The first four chap- ters deal with general principles and definitions in occupational epi demioligy and describe the work-related hazards and diseases. Chapter 5,6 and 7 deal with information collection and the use of data in the assessment of health risks and in descriptive epidemiology. General methods for epidemiological studies are discussed. The following chap ters address specific aspects such as the study of combined effects, the statistical analysis of epidemiological data, the validity as- pects of epidemiological studies, including consideration on the pro- blems of 'false positive' and 'false negative' results and the basis for causality judgment or the particular interest of experimental epi demiology in occupational health. Chapters cover two special issues of importance to workers' health, namely occupational stress and the epidemiology of accidents.
Biostatistics for Epidemiologists is a unique book that provides a collection of methods that can be used to analyze data in most epidemiological studies. It examines the theoretical background of the methods described and discusses general principles that apply to the analysis of epidemiological data. Specific topics addressed include statistical interference in epidemiological research, important methods used for analyzing epidemiological data, multivariate models, dose-response analysis, analysis of the interaction between causes of disease, meta-analysis, and computer programs. Biostatistics for Epidemiologists will be a useful guide for all epidemiologists and public health professionals who rely on biostatistical data in their work.