The case-control approach is a powerful method for investigating factors that may explain a particular event. It is extensively used in epidemiology to study disease incidence, one of the best-known examples being Bradford Hill and Doll's investigation of the possible connection between cigarette smoking and lung cancer. More recently, case-control studies have been increasingly used in other fields, including sociology and econometrics. With a particular focus on statistical analysis, this book is ideal for applied and theoretical statisticians wanting an up-to-date introduction to the field. It covers the fundamentals of case-control study design and analysis as well as more recent developments, including two-stage studies, case-only studies and methods for case-control sampling in time. The latter have important applications in large prospective cohorts which require case-control sampling designs to make efficient use of resources. More theoretical background is provided in an appendix for those new to the field.

Covers the fundamentals of case-control studies including important recent developments, with a focus on statistical analysis.

Planning clinical research requires many decisions. The authors of this book explain key decisions with examples showing what works and what does not.

Handbook of Statistical Methods for Case-Control Studies is written by leading researchers in the field. It provides an in-depth treatment of up-to-date and currently developing statistical methods for the design and analysis of case-control studies, as well as a review of classical principles and methods. The handbook is designed to serve as a reference text for biostatisticians and quantitatively-oriented epidemiologists who are working on the design and analysis of case-control studies or on related statistical methods research. Though not specifically intended as a textbook, it may also be used as a backup reference text for graduate level courses. Book Sections Classical designs and causal inference, measurement error, power, and small-sample inference Designs that use full-cohort information Time-to-event data Genetic epidemiology About the Editors Ørnulf Borgan is Professor of Statistics, University of Oslo. His book with Andersen, Gill and Keiding on counting processes in survival analysis is a world classic. Norman E. Breslow was, at the time of his death, Professor Emeritus in Biostatistics, University of Washington. For decades, his book with Nick Day has been the authoritative text on case-control methodology. Nilanjan Chatterjee is Bloomberg Distinguished Professor, Johns Hopkins University. He leads a broad research program in statistical methods for modern large scale biomedical studies. Mitchell H. Gail is a Senior Investigator at the National Cancer Institute. His research includes modeling absolute risk of disease, intervention trials, and statistical methods for epidemiology. Alastair Scott was, at the time of his death, Professor Emeritus of Statistics, University of Auckland. He was a major contributor to using survey sampling methods for analyzing case-control data. Chris J. Wild is Professor of Statistics, University of Auckland. His research includes nonlinear regression and methods for fitting models to response-selective data.

Sixth edition of the hugely successful, internationally recognised textbook on global public health and epidemiology, with 3 volumes comprehensively covering the scope, methods, and practice of the discipline

This workbook is designed to teach the major fundamental concepts in Epidemiology, Biostatistics, and clinical research design alongside the textbook "Epidemiology and Biostatistics, 2nd Edition". It is written in concise and organized fashion with many examples to illustrate the concepts deriving from a collection of written materials created to teach Epidemiology and Biostatistics to medical students. The major differences from related titles include a “story” based approach toward teaching the material, relative brevity while maintaining focus on key concepts, and taking the perspective of first-time learners (avoiding and/or clearly defining jargon, using clear common-sense language). It features a variety of questions: long, short, and multiple choice questions. The workbook is made to provide students with the tools necessary to form their own informed conclusions from the clinical research literature.

This useful reference provides solid knowledge of the risks and benefits associated with the cancer screening process, assesses abnormal results and therapeutic outcomes, and facilitates the communication of these issues to patients. Describes screening tests from individual, health care, ethical, legal, and regulatory perpectives! Gathering insights from over 35 international experts in the field, Cancer Screening details the screening procedures available for a wide variety of cancers offers a practical approach to screening implementation for a number of cancer sites discusses the explicit methodology of judging screening tests reports screening recommendations from various organizations analyzes the strengths and hazards of current screening procedures as well as the quality of supporting evidence appraises the utility of screening tests versus other health care strategies presents a basis for judging future screening technologies such as genetic testing and more! Including over 1300 references, tables, and figures, Cancer Screening is an indispensable guide for basic and clinical oncologists, internists and family practitioners, gynecologists, public health physicians, health policy specialists, health economists, health educators, prevention and early detection advocates, epidemiologists, biometricians, statisticians, and medical school and graduate students in these disciplines.

The Encyclopedic Reference of Public Health presents the most important definitions, principles and general perspectives of public health, written by experts of the different fields. The work includes more than 2,500 alphabetical entries. Entries comprise review-style articles, detailed essays and short definitions. Numerous figures and tables enhance understanding of this little-understood topic. Solidly structured and inclusive, this two-volume reference is an invaluable tool for clinical scientists and practitioners in academia, health care and industry, as well as students, teachers and interested laypersons.

A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usually covered in public health courses including (i) Applied Regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health. In addition, this book: • Is based on the authors’ course notes from 20 years teaching regression modeling in public health courses • Provides exercises at the end of each chapter • Contains a solutions chapter with answers in STATA, SAS, SPSS, and R • Provides real-world public health applications of the theoretical aspects contained in the chapters Applications of Regression Models in Epidemiology is a reference for graduate students in public health and public health practitioners. ERICK SUÁREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. He received a Ph.D. degree in Medical Statistics from the London School of Hygiene and Tropical Medicine. He has 29 years of experience teaching biostatistics. CYNTHIA M. PÉREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. She received an M.S. degree in Statistics and a Ph.D. degree in Epidemiology from Purdue University. She has 22 years of experience teaching epidemiology and biostatistics. ROBERTO RIVERA is an Associate Professor at the College of Business at the University of Puerto Rico at Mayaguez. He received a Ph.D. degree in Statistics from the University of California in Santa Barbara. He has more than five years of experience teaching statistics courses at the undergraduate and graduate levels. MELISSA N. MARTÍNEZ is an Account Supervisor at Havas Media International. She holds an MPH in Biostatistics from the University of Puerto Rico and an MSBA from the National University in San Diego, California. For the past seven years, she has been performing analyses for the biomedical research and media advertising fields.