This book covers classic epidemiological designs that use a reference/control group, including case-control, case-cohort, nested case-control and variations of these designs, such as stratified and two-stage designs. It presents a unified view of these sampling designs as representations of an underlying cohort or target population of interest. This enables various extended designs to be introduced and analysed with a similar approach: extreme sampling on the outcome (extreme case-control design) or on the exposure (exposure-enriched, exposure-density, countermatched), designs that re-use prior controls and augmentation sampling designs. Further extensions exploit aggregate data for efficient cluster sampling, accommodate time-varying exposures and combine matched and unmatched controls. Self-controlled designs, including case-crossover, self-controlled case series and exposure-crossover, are also presented. The test-negative design for vaccine studies and the use of negative controls for bias assessment are introduced and discussed.
This book is intended for graduate students in biostatistics, epidemiology and related disciplines, or for health researchers and data analysts interested in extending their knowledge of study design and data analysis skills.
This book
- Bridges the gap between epidemiology and the more mathematically oriented biostatistics books.
- Assembles the wealth of epidemiological knowledge about observational study designs that is scattered over several decades of scientific publications.
- Illustrates the performance of methods in real research applications.
- Provides guidelines for implementation in standard software packages (Stata, R).
- Includes numerous exercises, covering simple mathematical proofs, consideration of proposed or published designs, and practical data analysis.
1. Classical Epidemiology Designs 2. From Tables to Logistic Regression Models 3. Extensions to Classical Epidemiological Studies 4. Including Time: Cox Regression and Related 5. Estimates Available from Standard Designs 6. Estimates from Matched and Nested Designs 7. Reusing Case-Control Data 8. More Complex Designs 9. More Complex Data Structures 10. Other Controlled Epidemiological Studies
Biography
Marie Reilly obtained a PhD in Biostatistics from the University of Washington and has been a Professor of Biostatistics at the Department of Medical Epidemiology and Biostatistics at the Karolinska Institute, Stockholm since 2003. Much of her methodological work involved the development of methods for extending standard epidemiological designs and analysis to address problems arising in research studies using Swedish national registers. Applications spanned a wide range of research investigations in perinatal epidemiology, family studies, blood safety, transfusion studies and maternal screening. Her applied work has also involved engagement in a number of HIV studies in Africa, including randomized clinical trials of candidate HIV-vaccines in infants and adults.
At the Karolinska Institute, she was responsible for the design and delivery of biostatistics education to PhD students in epidemiology and to undergraduate medical students. This book began with these teaching materials and grew over several years with advanced courses, in Karolinska and overseas, on extended case-control designs and re-use of case-control data.
"The author has done a wonderful job of selecting the topics and the illustrative examples. The emphasis of the book is less on rigorous mathematical details and more on ideas and practical implementation.
This book will be suitable as a text for master’s and Ph.D. students in epidemiology and biostatistics. Researchers interested in learning more about design and analysis of epidemiological studies will find this book useful as well."
Kaushik Ghosh, University of Nevada, Las Vegas, U.S.A, Journal of the American Statistical Association, January 2024.
