Analysis of Epidemiologic Data Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analysis of Epidemiologic Data Using R write by Robert Hirsch. This book was released on 2023-09-10. Analysis of Epidemiologic Data Using R available in PDF, EPUB and Kindle. This book addresses the description and analysis of occurrence data frequently encountered in epidemiological studies. With the occurrence of Covid-19, people have been exposed to the analysis and interpretation of epidemiological data. To be informed consumers of this information, people need to understand the nature and analysis of these data. Effort is made to emphasize concepts rather than mathematics. Subjects range from description of the frequencies of disease to the analysis of associations between the occurrence of disease and exposure. Those analyses begin with simple associations and work up to complex relationships that involve the control of extraneous characteristics. Analyses rely on the statistical software R, which is freeware in wide use by professional epidemiologists and other scientists.
Applying Quantitative Bias Analysis to Epidemiologic Data
Applying Quantitative Bias Analysis to Epidemiologic Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applying Quantitative Bias Analysis to Epidemiologic Data write by Timothy L. Lash. This book was released on 2011-04-14. Applying Quantitative Bias Analysis to Epidemiologic Data available in PDF, EPUB and Kindle. Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.
Analysis of Epidemiological Data Using R and Epicalc
Analysis of Epidemiological Data Using R and Epicalc - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analysis of Epidemiological Data Using R and Epicalc write by Virasakdi Chongsuvivatwong. This book was released on 2008. Analysis of Epidemiological Data Using R and Epicalc available in PDF, EPUB and Kindle.
Biostatistics for Epidemiology and Public Health Using R
Biostatistics for Epidemiology and Public Health Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Biostatistics for Epidemiology and Public Health Using R write by Bertram K.C. Chan, PhD. This book was released on 2015-11-05. Biostatistics for Epidemiology and Public Health Using R available in PDF, EPUB and Kindle. 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. In addition to being freely available, R offers several advantages for biostatistics, including strong graphics capabilities, the ability to write customized functions, and its extensibility. 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-bystep 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 and exercises 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. This text is supplemented with teaching resources, including an online guide for students in solving exercises and an instructor's manual. 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 and exercises to illustrate the application of R language for biostatistical calculations and graphical displays of results Includes online student solutions guide and instructor's manual
Epidemics
Epidemics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Epidemics write by Ottar N. Bjørnstad. This book was released on 2018-10-30. Epidemics available in PDF, EPUB and Kindle. This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in ‘consumer-resource metapopulations’. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and ‘models-with-data’ have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and ‘models-and-data’ to understand epidemics and infectious disease dynamics in space and time.
