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A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY
Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach.
Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions.
Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach.
Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions.
A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY
Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach.
Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions.
Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach.
Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions.
Über den Autor
DANIEL WESTREICH received his B.S. in computer science from Yale University, and -- after a short stint as a software engineer at Microsoft -- a Ph.D. in epidemiology from the University of North Carolina, Chapel Hill. He is currently an associate professor of epidemiology at UNC, where his work focuses substantively at the intersection of HIV and women's reproductive health. He has served as a member-at-large of the executive board of the Society for Epidemiologic Research and in editorial capacities at the American Journal of Epidemiology and Epidemiology. In 2014 Dr. Westreich was awarded an NIH DP2 New Innovator Award to develop epidemiologic methods for implementation science.
Inhaltsverzeichnis
- Acknowledgements
- Overview
- 1. Measuring Disease
- 2. Measures of Association
- 3. Causal Inference, Causal Effect Estimation, and Systematic Error
- 4. Diagnostic Testing, Screening, and Surveillance
- 5. Randomized Trials
- 6. Observational Cohort Studies
- 7. Case-Control Studies
- 8. Other Study Designs
- 9. Causal Impact, From Exposures to Interventions
- Index
Details
Erscheinungsjahr: | 2019 |
---|---|
Fachbereich: | Therapie |
Genre: | Medizin |
Rubrik: | Wissenschaften |
Medium: | Taschenbuch |
Inhalt: | Kartoniert / Broschiert |
ISBN-13: | 9780190665760 |
ISBN-10: | 0190665769 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Westreich, Daniel |
Hersteller: | Hurst & Co. |
Maße: | 233 x 158 x 17 mm |
Von/Mit: | Daniel Westreich |
Erscheinungsdatum: | 13.11.2019 |
Gewicht: | 0,335 kg |
Über den Autor
DANIEL WESTREICH received his B.S. in computer science from Yale University, and -- after a short stint as a software engineer at Microsoft -- a Ph.D. in epidemiology from the University of North Carolina, Chapel Hill. He is currently an associate professor of epidemiology at UNC, where his work focuses substantively at the intersection of HIV and women's reproductive health. He has served as a member-at-large of the executive board of the Society for Epidemiologic Research and in editorial capacities at the American Journal of Epidemiology and Epidemiology. In 2014 Dr. Westreich was awarded an NIH DP2 New Innovator Award to develop epidemiologic methods for implementation science.
Inhaltsverzeichnis
- Acknowledgements
- Overview
- 1. Measuring Disease
- 2. Measures of Association
- 3. Causal Inference, Causal Effect Estimation, and Systematic Error
- 4. Diagnostic Testing, Screening, and Surveillance
- 5. Randomized Trials
- 6. Observational Cohort Studies
- 7. Case-Control Studies
- 8. Other Study Designs
- 9. Causal Impact, From Exposures to Interventions
- Index
Details
Erscheinungsjahr: | 2019 |
---|---|
Fachbereich: | Therapie |
Genre: | Medizin |
Rubrik: | Wissenschaften |
Medium: | Taschenbuch |
Inhalt: | Kartoniert / Broschiert |
ISBN-13: | 9780190665760 |
ISBN-10: | 0190665769 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Westreich, Daniel |
Hersteller: | Hurst & Co. |
Maße: | 233 x 158 x 17 mm |
Von/Mit: | Daniel Westreich |
Erscheinungsdatum: | 13.11.2019 |
Gewicht: | 0,335 kg |
Warnhinweis