Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems.
New to the Third Edition
After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding.
Web Resource
A wealth of supporting material can be downloaded from the book’s CRC Press web page, including:
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Mark Woodward is a professor of statistics and epidemiology at the University of Oxford, a professor of biostatistics in the George Institute at the University of Sydney, and an adjunct professor of epidemiology at Johns Hopkins University.
Review:"This text, like its predecessors, hits the mark. ... The author writes extremely well and the text is resplendent with exercises. It would be a crime if Epidemiology: Study Design and Data Analysis were never used as a text! ... I wish a text like this had been available for my coursework. Enhancing its value as a text, it will be extremely useful as a reference book for its intended audience―researchers and applied statisticians. ... the only excuse for an epidemiologist or applied statistician not to have it on his or her bookshelf is that he or she has not seen or heard of it. Make this book your next purchase!"
―Gregory E. Gilbert, The American Statistician, November 2014
Praise for Previous Editions:
"As a text in quantitative epidemiology, this book also works nicely as a text in biostatistics...The presentation style is relaxed, the examples are helpful, and the level of technical difficulty makes the material approachable without oversimplification...It is sufficiently broad and deep in coverage to compete with standard texts in the field and has the added bonus of emphasizing study design. Methods and issues related to designs commonly used in a wide variety of health sciences are included..."
-Ken Hess, Department of Biomathematics and Biostatistics, Anderson Cancer Center
"The second edition of this epidemiology text is strengthened to cater to the two audiences the author has in mind: applied statisticians wishing to learn how their statistical expertise can be used in the epidemiology field and statistic-curious researchers who want to understand how statistical techniques can be used to solve epidemiological problems. ...The result is a book that will invariably appeal to the intended audience, one with practical applications of techniques and interpretations of results in an epidemiological context. ...The book is most certainly an ambitious attempt at covering a broad array of the most important epidemiologic study designs and analytical methods. This is further enforced by the addition of the meta-analysis chapter. ...This book will be valuable to statisticians in applying their discipline to epidemiology. Mark Woodward's excellent second edition will effectively serve post-graduate or advanced undergraduate students studying epidemiology, as well as statisticians or researchers who are regularly confronted with epidemiological questions."
-Journal of the American Statistical Association
"This book provides very good coverage of major issues in the design of epidemiological studies, and a decent, but very quick, tour of commonly used statistical models for such studies."
-Short Book Reviews Publication of the International Statistical Institute, K.S. Brown, University of Waterloo, Canada
"Amazingly, Woodward manages to describe quite sophisticated models and analysis with nothing more complicated than summation signs. ...I highly recommend it."
-Statistics in Medicine, 2006
"The second edition of this concisely written book covers all statistical methods being of relevance for the planning and analysis of epidemiological studies where the author avoids unnecessary mathematical details for the sake of comprehensibility. The presented statistical principles are always carefully discussed in the context of epidemiological concepts, for instance depending on the different study designs. Detailed practical examples coming from real studies as far as possible illustrate their application. ...The book can be highly recommended to researchers in epidemiology who want to understand better the statistical principles being typically applied in this field and to statisticians who want to understand more about statistics in epidemiology, but also to graduate students in epidemiology, public health, medical research and statistics."
-Biometrics, Sept. 2005
"I think anyone with an interest in both biostatistics and epidemiology will want a copy this book on their bookshelf ... it is a first-rate reference book."
"I find Professor Woodward's text the most complete and practical introduction to the design and analysis of epidemiological studies I've encountered... an excellent text for either a course introducing epidemiologists to statistical thought and methods or a course introducing statisticians to epidemiological thought and methods... students appreciate having a readable textbook replete with understandable examples and worked exercises...offers a complete introduction to statistical and epidemiological methods in the study of disease in human populations. All of the standard topics are included, and the second edition even has a chapter on meta-analysis. ...This book can be used as a text to introduce epidemiological methods to graduate students in statistics who have no background in epidemiology, or vice versa...Professor Woodward is to be congratulated on a job well done."
-Dan McGee, Dept of Statistics, Florida State University
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Buchbeschreibung Crc Pr Inc, 2014. Gebundene Ausgabe. Buchzustand: Neu. Neu Neuware, Importqualität, auf Lager, Sofortversand - FUNDAMENTAL ISSUES What is Epidemiology Case Studies: The Work of Doll and Hill Populations and Samples Measuring Disease Measuring the Risk Factor Causality Studies Using Routine Data Study Design Data Analysis Exercises BASIC ANALYTICAL PROCEDURES Introduction Case Study Types of Variables Tables and Charts Inferential Techniques for Categorical Variables Descriptive Techniques for Quantitative Variables Inferences about Means Inferential Techniques for Non-Normal Data Measuring Agreement Assessing Diagnostic Tests Exercises ASSESSING RISK FACTORS Risk and Relative Risk Odds and Odds Ratio Relative Risk or Odds Ratio Prevalence Studies Testing Association Risk Factors Measured at Several Levels Attributable Risk Rate and Relative Rate Measures of Difference EPITAB Commands in Stata Exercises CONFOUNDING AND INTERACTION Introduction The Concept of Confounding Identification of Confounders Assessing Confounding Standardization Mantel-Haenszel Methods The Concept of Interaction Testing for Interaction Dealing with Interaction EPITAB Commands in Stata Exercises COHORT STUDIES Design Considerations Analytical Considerations Cohort Life Tables Kaplan-Meier Estimation Comparison of Two Sets of Survival Probabilities Competing Risk The Person-Years Method Period-Cohort Analysis Exercises CASE-CONTROL STUDIES Basic Design Concepts Basic Methods of Analysis Selection of Cases Selection of Controls Matching The Analysis of Matched Studies Nested Case-Control Studies Case-Cohort Studies Case-Crossover Studies Exercises INTERVENTION STUDIES Introduction Ethical Considerations Avoidance of Bias Parallel Group Studies Cross-Over Studies Sequential Studies Allocation to Treatment Group Trials as Cohorts Exercises SAMPLE SIZE DETERMINATION Introduction Power Testing a Mean Value Testing a Difference between Means Testing a Proportion Testing a Relative Risk Case-Control Studies Complex Sampling Designs Concluding Remarks Exercises MODELING QUANTITATIVE OUTCOME VARIABLES Statistical Models One Categorical Explanatory Variable One Quantitative Explanatory Variable Two Categorical Explanatory Variables Model Building General Linear Models Several Explanatory Variables Model Checking Confounding Splines Panel Data Non-Normal Alternatives Exercises MODELING BINARY OUTCOME DATA Introduction Problems with Standard Regression Models Logistic Regression Interpretation of Logistic Regression Coefficients Generic Data Multiple Logistic Regression Models Tests of Hypotheses Confounding Interaction Dealing with a Quantitative Explanatory Variable Model Checking Measurement Error Case-Control Studies Outcomes with Several Levels Longitudinal Data Binomial Regression Propensity Scoring Exercises MODELING FOLLOW-UP DATA Introduction Basic Functions of Survival Time Estimating the Hazard Function Probability Models Proportional Hazards Regression Models The Cox Proportional Hazards Model The Weibull Proportional Hazards Model Model Checking Competing Risk Poisson Regression Pooled Logistic Regression Exercises META-ANALYSIS Reviewing Evidence Systematic Review A General Approach to Pooling Investigating Heterogeneity Pooling Tabular Data Individual Participant Data Dealing with Aspects of Study Quality Publication Bias Advantages and Limitations of Meta-Analysis Exercises RISK SCORES AND CLINICAL DECISION RULES Introduction Association and Prognosis Risk Scores from Statistical Models Quantifying Discrimination Calibration Recalibration The Accuracy of Predictions Assessing an Extraneous Prognostic Variable Reclassification Validation Presentation of Risk Scores Impact Studies Exercises COMPUTER-INTENSIVE METHODS Rationale The Bootstrap Bootstrap Confidence Intervals Practical Issues When Bootstrapping Further Examples of Bootstrapping Bootstrap Hypothesis Testing Limitations of Bootstrapping Permutation Tests Missing Values Naive Imputation Methods Univariate Multiple Imputation Multivariate Multiple I. Artikel-Nr. INF1000255898
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