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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. This book is intended for graduate students, health researchers and data analysts.
List of contents
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
About the author
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.
Summary
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. This book is intended for graduate students, health researchers and data analysts.
