Biostatistics And Computer Based Analysis Of Health Data Using R

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Biostatistics And Computer Based Analysis Of Health Data Using R

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Author : Christophe Lalanne
language : en
Publisher: Elsevier
Release Date : 2016-07-13

Biostatistics And Computer Based Analysis Of Health Data Using R written by Christophe Lalanne and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-13 with Computers categories.

Biostatistics and Computer-Based Analysis of Health Data Using the R Software addresses the concept that many of the actions performed by statistical software comes back to the handling, manipulation, or even transformation of digital data. It is therefore of primary importance to understand how statistical data is displayed and how it can be exploited by software such as R. In this book, the authors explore basic and variable commands, sample comparisons, analysis of variance, epidemiological studies, and censored data. With proposed applications and examples of commands following each chapter, this book allows readers to apply advanced statistical concepts to their own data and software. Features useful commands for describing a data table composed made up of quantitative and qualitative variables Includes measures of association encountered in epidemiological studies, odds ratio, relative risk, and prevalence Presents an analysis of censored data, the key main tests associated with the construction of a survival curve (log-rank test or Wilcoxon), and the Cox regression model

Biostatistics And Computer Based Analysis Of Health Data Using Stata

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Author : Christophe Lalanne
language : en
Publisher: Elsevier
Release Date : 2016-09-06

Biostatistics And Computer Based Analysis Of Health Data Using Stata written by Christophe Lalanne and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-06 with Computers categories.

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research. The use of Stata for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epideomological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential Stata packages and commands. Provides detailed examples of the use of Stata for common biostatistical tasks in medical research Features a work program structured around the four previous chapters and a series of practical exercises with commented corrections Includes an appendix to help the reader familiarize themselves with additional packages and commands Focuses on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data

Biostatistics And Computer Based Analysis Of Health Data Using Sas

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Author : Christophe Lalanne
language : en
Publisher: Elsevier
Release Date : 2017-06-22

Biostatistics And Computer Based Analysis Of Health Data Using Sas written by Christophe Lalanne and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-22 with Mathematics categories.

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.The use of SAS for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with a basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epidemiological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential SAS commands. Presents the use of SAS software in the statistical approach for the management of data modeling Includes elements of the language and descriptive statistics Supplies measures of association, comparison of means, and proportions for two or more samples Explores linear and logistic regression Provides survival data analysis

Biostatistics For Epidemiology And Public Health Using R

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Author : Bertram K.C. Chan, PhD
language : en
Publisher: Springer Publishing Company
Release Date : 2015-11-05

Biostatistics For Epidemiology And Public Health Using R written by Bertram K.C. Chan, PhD and has been published by Springer Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-05 with Medical categories.

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

R For Health Data Science

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Author : Ewen Harrison
language : en
Publisher: CRC Press
Release Date : 2020-12-31

R For Health Data Science written by Ewen Harrison and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-31 with Medical categories.

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.

Analysis In Nutrition Research

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Author : George Pounis
language : en
Publisher: Academic Press
Release Date : 2018-10-19

Analysis In Nutrition Research written by George Pounis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Health & Fitness categories.

Analysis in Nutrition Research: Principles of Statistical Methodology and Interpretation of the Results describes, in a comprehensive manner, the methodologies of quantitative analysis of data originating specifically from nutrition studies. The book summarizes various study designs in nutrition research, research hypotheses, the proper management of dietary data, and analytical methodologies, with a specific focus on how to interpret the results of any given study. In addition, it provides a comprehensive overview of the methodologies used in study design and the management and analysis of collected data, paying particular attention to all of the available, modern methodologies and techniques. Users will find an overview of the recent challenges and debates in the field of nutrition research that will define major research hypotheses for research in the next ten years. Nutrition scientists, researchers and undergraduate and postgraduate students will benefit from this thorough publication on the topic. Provides a comprehensive presentation of the various study designs applied in nutrition research Contains a parallel description of statistical methodologies used for each study design Presents data management methodologies used specifically in nutrition research Describes methodologies using both a theoretical and applied approach Illustrates modern techniques in dietary pattern analysis Summarizes current topics in the field of nutrition research that will define major research hypotheses for research in the next ten years

Clinical Trial Data Analysis Using R

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Author : Ding-Geng (Din) Chen
language : en
Publisher: CRC Press
Release Date : 2010-12-14

Clinical Trial Data Analysis Using R written by Ding-Geng (Din) Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-14 with Mathematics categories.

Too often in biostatistical research and clinical trials, a knowledge gap exists between developed statistical methods and the applications of these methods. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book’s practical, detailed approach draws on the authors’ 30 years of real-world experience in biostatistical research and clinical development. Each chapter presents examples of clinical trials based on the authors’ actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data. With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.

Introduction To Data Analysis And Graphical Presentation In Biostatistics With R

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Author : Thomas W. MacFarland
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-19

Introduction To Data Analysis And Graphical Presentation In Biostatistics With R written by Thomas W. MacFarland and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-19 with Medical categories.

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.

Analysis Of Epidemiologic Data Using R

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Author : Robert Hirsch
language : en
Publisher: Springer Nature
Release Date : 2023-09-10

Analysis Of Epidemiologic Data Using R written by Robert Hirsch and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-10 with Science categories.

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.

Statistical Analysis Of Microbiome Data With R

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Author : Yinglin Xia
language : en
Publisher: Springer
Release Date : 2018-10-20

Statistical Analysis Of Microbiome Data With R written by Yinglin Xia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-20 with Computers categories.

This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.