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This guide is intended to assist you with the concepts presented in NUR 520: Epidemiological & Biostatistical Applications in Healthcare. Within this guide, you will find resources that explain research methods and sources of data that you will encounter in the medical field.
from Gale Encyclopedia of Public Health
"Epidemiology is the branch of medicine and public health that concerns the study of factors associated with either communicable or noncommunicable diseases that affect the health of populations rather than individuals." Read more about epidemiology This link opens in a new window.
Modern Epidemiology by
Selected as a Doody's Core Title for 2022! Now in a fully revised 4th Edition, Modern Epidemiology remains the gold standard text in this complex and evolving field, offering unparalleled, comprehensive coverage of the principles and methods of epidemiologic research. Featuring a new, full-color design, updated models, and a new format allowing space for margin notes, this edition continues to provide authoritative information on the methodologic issues crucial to the wide range of epidemiologic applications in public health and medicine. Reflects both the conceptual development of this evolving science and the increasing role that epidemiology plays in both public health and medicine. Features a new full-color design, new coverage of marginal structural models, new instrumental variable analysis, updated structural nested models, and more. Covers a broad range of concepts and methods, including epidemiologic measures of occurrence and effect, study designs, validity, precision, statistical interference, field methods, and causal diagrams. Includes data analysis topics such as Bayesian analysis, sensitivity analysis, and bias analysis, with an extensive overview of modern regression methods including logistic and survival regression, splines, hierarchical (multilevel) regression, propensity scores and other scoring methods, and g-estimation. Discusses special topics such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, clinical epidemiology, and meta-analysis. Coauthored by three leading epidemiologists, with contributions from experts in a variety of epidemiologic sub-disciplines.
from International Encyclopedia of Public Health
"The role of statistics in public health research and practice is much as it is with other sciences; its methods assist in the design of studies and in their interpretation by dealing probabilistically with variation in measurements." Read more about biostatistics This link opens in a new window.
Biostatistics for Epidemiology and Public Health Using R by
Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-by-step approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of R. These examples illustrate the replication of R for biostatistical calculations and graphical display of results. The text covers both essential and advanced techniques and applications in biostatistics that are relevant to epidemiology. Also included are an instructor's guide, student solutions manual, and downloadable data sets. Key Features: First overview biostatistics textbook for epidemiology and public health that uses the open-source R program Covers essential and advanced techniques and applications in biostatistics as relevant to epidemiology Features abundant examples to illustrate the application of R language for biostatistical calculations and graphical displays of results Includes instructor's guide, student solutions manual, and downloadable data sets.
