Full Description
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.
Contents
Chapter 1. Descriptive and Analytical Epidemiology .- Chapter 2. Cross-Sectional Study: The Role of Observation in Epidemiological Studies.- Chapter 3. Case-Control Study .- Chapter 4. Cohort Studies .- Chapter 5. Epidemiological Measures.- Chapter 6. Clinical Trials .- Chapter 7. Screening.- Chapter 8. Surveillance: The Role of Observation in Epidemiological Studies .- Chapter 9. Standardization .- Chapter 10. Causal Association .- Chapter 11. Bias, Confounding, and Effect Modifier .- Chapter 12. Epidemic Investigation and Control .- Chapter 13. Population Projection.- Chapter 14. Geospatial Applications in Epidemiology: Location, Location, Location .- Chapter 15. Survival Analysis and Applications Using SAS and SPSS .- Chapter 16. Systematic Review and Meta-Analysis: Evidence-based Decision-Making in Public Health.- Chapter 17. Sample Size Estimation .- Chapter 18. Missing Data Imputation: A Practical Guide .- Chapter 19. Artificial Intelligence and Machine Learning .- Chapter 20. A Step-By-Step Guide to Data Analysis Using SPSS: Iron Study Data.- Chapter 21. Data Analysis Using SPSS: Jackson Heart Study .- Chapter 22. Multivariate Linear Regression and Logistics Regression Analysis Using SAS.- Chapter 23. Epidemiology of COVID-19: An Update .
