 Название: R for Basic Biostatistics in Medical Research
Автор: Anand Srinivasan, Archana Mishra, Praveen Kumar-M
Издательство: Springer
Год: 2024
Страниц: 307
Язык: английский
Формат: pdf (true), epub
Размер: 29.3 MB
The scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R.
Ross Ihaka and Robert Gentleman developed a statistical analysis and graphics system known as R, which is a dialect of the language S. R is regarded as both a language and software. It is a powerful open-source programming language and environment for statistical computing, and graphics offers a versatile toolkit that resonates particularly well with the complex and data-intensive nature of medical research. The application of R language in medical research has become indispensable, addressing the growing need for sophisticated data analysis and statistical modeling in the healthcare domain.
R facilitates the creation of reproducible and transparent analyses, which is critical for ensuring the reliability of findings. Its graphics and visualization capabilities enable researchers to present complex data in a comprehensible manner, aiding in the communication of results to both scientific audiences and healthcare practitioners. The collaborative nature of R’s open-source community further enhances its appeal in medical research. Researchers can access a vast repository of packages and scripts developed by the global community, accelerating the implementation of cutting-edge methodologies and ensuring that the latest statistical techniques are readily available for analysis in the healthcare domain. In a nutshell, learning R can be essential for several reasons, particularly for individuals involved in data analysis, statistics, and programming.
R is a programming language, and learning it can provide a strong foundation in programming concepts and data manipulation. This knowledge can be advantageous for individuals interested in software development, especially in roles that involve data-centric applications.
Contents:
1. Why R Is Essential? What Are the Prospects of Learning R?
2. An Overview of Statistical Analysis Plan for Clinical Studies
3. Introduction to R Environment and Basic Commands
4. Data Handling and Manipulation in R with Descriptive Statistics
5. Introduction to Packages in R: Installation, Loading, Unloading and Deletion
6. Visualization of dаta: Basic and Advanced
7. Inferential Statistics for Hypothesis Testing of Parametrically Distributed Data
8. Inferential Statistics for the Hypothesis Testing of Non-parametric Data
9. Computation of Sample Size for Clinical Studies
10. Correlation and Linear Regression Analysis for Continuous Outcome
11. Logistic Regression Analysis for Categorical Outcome
12. Receiver Operating Characteristic (ROC) Curve Analysis for Diagnostic Studies
13. Survival Analysis for Time to Event-Based Outcome
14. Conducting Randomization in Clinical Trials
15. Development of Web-Based Interactive Servers Using R Shiny Package
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