Buch, Englisch, 445 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 6905 g
Buch, Englisch, 445 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 6905 g
Reihe: Lecture Notes in Statistics - Proceedings
ISBN: 978-1-4614-8980-1
Verlag: Springer
Methods of risk analysis and the outcome of particular evaluations and predictions are covered in detail in this proceedings volume, whose contributions are based on invited presentations from Professor Mei-Ling Ting Lee's 2011 symposium on Risk Analysis and the Evaluation of Predictions. This symposium was held at the University of Maryland in October of 2011. Risk analysis is the science of evaluating health, environmental, and engineering risks resulting from past, current, or anticipated, future activities. The use of these evaluations include to provide information for determining regulatory actions to limit risk, present scientific evidence in legal settings, evaluate products and potential liabilities within private organizations, resolve World Trade disputes amongst nations, and educate the public concerning particular risk issues. Risk analysis is an interdisciplinary science that relies on epidemiology and laboratory studies, collection of exposure and other field data, computer modeling, and related social, economic and communication considerations. In addition, social dimensions of risk are addressed by social scientists.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Präventivmedizin, Gesundheitsförderung, Medizinisches Screening
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
Weitere Infos & Material
Preface.- Methods for Evaluating Prediction Performance of Biomarkers and Tests.- Multiple Imputation Approach for Surrogate Marker Evaluation in the Principal Strati cation Causal Inference Framework.
