This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a “hands on” approach to the use of SAS for a broad number of epidemiologic analyses, readers learn techniques for data entry and cleaning, categorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps for successful programming. SAS (Statistical Analysis System) is an integrated system of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engage in many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widely used in the fields of epidemiology and public health research, predominately due to its ability to reliably analyze very large administrative data sets, as well as more commonly encountered clinical trial and observational research data.

This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a “hands on” approach to the use of SAS for a broad number of epidemiologic analyses, readers learn techniques for data entry and cleaning, categorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps for successful programming. SAS (Statistical Analysis System) is an integrated system of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engage in many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widely used in the fields of epidemiology and public health research, predominately due to its ability to reliably analyze very large administrative data sets, as well as more commonly encountered clinical trial and observational research data.

SAS Data Management for Public Health: An Introduction equips readers with the tools and knowledge they need to prepare public health data in SAS Data Management software for use in analysis. Highly accessible in nature, the book is specifically designed to help students who are new to SAS learn and master the system. The book is organized into 20 lessons. The opening lessons introduce SAS and provide tips and best practices for exploring data. Students are introduced to PROC MEANS, FREQ, UNIVARIATE, and PROC SGPLOT. They learn how to import data; merge, concatenate, and manage variables; perform data cleanup; and recode categorical and continuous variables. Specific lessons address comments, labels, and titles, formatting variables, conditional recoding, DO groups, arrays for recoding, and categorical data analysis. Closing lessons introduce stratified and subpopulation analysis, as well as logistic regression. The book includes an appendix to help students navigate and use SAS Studio. SAS Data Management for Public Health is an ideal resource for standalone courses in which SAS is taught or to complement any biostatistics or epidemiology course where students need to use SAS to analyze their data. Brianna Magnusson holds a Ph.D. in epidemiology and a M.P.H. from Virginia Commonwealth University. She is an associate professor in the Department of Public Health at Brigham Young University. Dr. Magnusson's research focuses on sexual and reproductive health with emphasis on factors influencing sexual decision-making. Caroline Stampfel holds an M.P.H. with a concentration in environmental epidemiology from the Yale School of Public Health. She serves as the director of programs for the Association of Maternal & Child Health Programs and leads a team of maternal and child health experts using data-driven, innovative approaches to improve the health and well-being of women, children, youth, families, and communities.

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

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 New chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines Many more exercises and examples using both Stata and SAS More than 60 new figures 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: Real-life data sets used in the text SAS and Stata programs used for examples in the text SAS and Stata programs for special techniques covered Sample size spreadsheet

Epidemiology is a subject of growing importance, as witnessed by its role in the description and prediction of the impact of new diseases such as AIDS and new-variant CJD. Epidemiology: Study Design and Data Analysis covers the whole spectrum of standard analytical techniques used in epidemiology, from descriptive techniques in report writing to model diagnostics from generalized linear models. The author discusses the advantages, disadvantages, and alternatives to case-control, cohort and intervention studies and details such crucial concepts as incidence, prevalence, confounding and interaction. Many exercises are provided, based on real epidemiological data sets collected from all over the world. The data sets are also available on an associated web site. Epidemiology: Study Design and Data Analysis will be an invaluable textbook for statistics and medical students studying epidemiology, and a standard reference for practicing epidemiologists.

The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology.

Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.

This textbook provides the basic concepts of epidemiology while preparing readers with the skills of applying statistical tools in real-life situations. Students, in general, struggle with statistical theories and their practical applications. This book makes statistical concepts easy to understand by focusing on real-life examples, case studies, and exercises. It also provides step-by-step guides for data analysis and interpretation using standard statistical software such as SPSS, SAS, R, Python, and GIS as appropriate, illustrating the concepts. Through the book's 23 chapters, readers primarily learn how to apply statistical methods in epidemiological studies and problem-solving. Among the topics covered: Clinical Trials Epidemic Investigation and Control Geospatial Applications in Epidemiology Survival Analysis and Applications Using SAS and SPSS Systematic Review and Meta-Analysis: Evidence-based Decision-Making in Public Health Missing Data Imputation: A Practical Guide Artificial Intelligence and Machine Learning Multivariate Linear Regression and Logistics Regression Analysis Using SAS Each chapter is written by eminent scientists and experts worldwide, including contributors from institutions in the United States, Canada, Bangladesh, India, Hong Kong, Malaysia, and the Middle East. Statistical Approaches for Epidemiology: From Concept to Application is an all-in-one book that serves as an essential text for graduate students, faculty, instructors, and researchers in public health and other branches of health sciences, as well as a useful resource for health researchers in industry, public health and health department professionals, health practitioners, and health research organizations and non-governmental organizations. The book also will be helpful for graduate students and faculty in related disciplines such as data science, nursing, social work, environmental health, occupational health, computer science, statistics, and biology.