医療とケアにおけるＲデータ分析 Data Analysis in Medicine and Health using R

著者名：Musa, Kamarul Imran/Mansor, Wan Nor Arifin Wan/Hanis, Tengku Muhammad

Chapman and Hall/CRC（2023/09/12発売）

• 言語：ENG
• ISBN：9781032284156
• eISBN：9781000957327

Description

Data analysis plays a vital role in guiding medical treatment plans, patient care, and the formulation of control and prevention policies in the field of healthcare. In today's era, researchers in these domains require a firm grasp of data, statistical concepts, and programming skills due to the increasing complexity of data. Reproducible analyses and cutting-edge statistical methods are becoming increasingly necessary.

This book, which is both comprehensive and highly practical, addresses these challenges by laying a solid foundation of data and statistical theory for readers. Subsequently, it equips them with practical skills to conduct analyses using the powerful R programming language, widely used by statisticians. The book takes a gentle approach to help readers navigate data and statistical analysis using R, minimizing the learning curve. RStudio is used as the integrated development environment (IDE) for enhanced productivity for readers to run their R codes.

Following a logical sequence commonly applied in medical and health research, the book covers fundamental concepts of data analysis and statistical modeling techniques. It provides readers, including those with limited statistical knowledge and programming skills, with hands-on experience through R programming.

The online version of this book is available on bookdown.org, a publishing platform provided by RStudio, PBC specifically designed to host books written using the "bookdown" package in R. Additionally, all R codes and datasets in this book can be found on the author's GitHub repository.

Table of Contents

1. R, RStudio and RStudio Cloud 2. R Scripts and R Packages 3. RStudio Project 4. Data Visualization 5. Data Wrangling 6. Exploratory Data Analysis 7. Linear Regression 8. Binary Logistic Regression 9. Multinomial Logistic Regression 10. Poisson Regression 11. Survival Analysis: Kaplan–Meier and Cox Proportional Hazard (PH) Regression 12. Parametric Survival Analysis 13. Introduction to Missing Data Analysis 14. Model Building and Variable Selection