Practical Statistics in Medicine with R
Understanding Fundamental Concepts through Examples
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 29 May 2026
- ISBN 9781032600581
- Binding Hardback
- No. of pages466 pages
- Size 254x178 mm
- Language English
- Illustrations 36 Illustrations, black & white; 156 Illustrations, color; 1 Halftones, black & white; 33 Halftones, color; 35 Line drawings, black & white; 123 Line drawings, color; 46 Tables, black & white 700
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Short description:
Whether you're new to statistical analysis or looking to enhance your analytical skills with the R programming language, this textbook provides comprehensive and practical guidance for understanding fundamental statistical concepts through healthcare examples in R.
MoreLong description:
Whether you’re new to statistical analysis or looking to enhance your analytical skills with the R programming language, this textbook provides comprehensive and practical guidance for understanding fundamental statistical concepts through healthcare examples in R. It is an ideal resource for students, educators, and healthcare researchers seeking a step-by-step first approach to effectively applying R in the analysis of healthcare data.
Readers are introduced to the fundamentals of base R, along with practical methods for data import, preprocessing, and transformation using functions from standard R packages such as base and stats, as well as pipe-friendly functions from the tidyverse collection of packages. Additionally, a chapter is devoted to visualization fundamentals, providing step-by-step guidance on creating data visualizations using the ggplot2 package and its extensions.
This textbook covers the most common statistical tests (e.g., t-test, one-way ANOVA, chisquare test, correlation, and non-parametric tests) and introduces more specialized analyses (e.g., linear regression, survival analysis, reliability of measurement analysis, diagnostic test accuracy, and ROC analysis) with examples from the biomedical field. Basic mathematical equations for these statistical tests and techniques are provided to enhance understanding. Statistical functions from both Base R and the rstatix add-on package are often presented side by side, fostering engagement and enriching the reader’s coding experience. Designed to be self-contained, this textbook does not require any prior experience with the R programming language, though it assumes a basic understanding of mathematics. (Note: Multivariable modeling and advanced statistical techniques are beyond the scope of this introductory textbook.)
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Table of Contents:
Preface About the Author 1 R via RStudio 2 RStudio Projects 3 R as calculator 4 R functions 5 R packages 6 R objects 7 Atomic vectors 8 Matrices and arrays 9 Lists and data frames 10 Data import, preprocessing, and transformation 11 Data visualization with ggplot2 12 Introduction to Statistics 13 Basic concepts of probability 14 Probability distributions 15 Descriptive statistics 16 Populations and samples 17 Confidence intervals 18 Hypothesis testing 19 Independent samples t-test 20 Wilcoxon-Mann-Whitney test 21 Paired samples t-test 22 Wilcoxon Signed-Rank test 23 One-way Analysis of Variance 24 Kruskal-Wallis test 25 Categorical data analysis 26 Correlation methods 27 Simple linear regression 28 Survival analysis 29 Reliability of measurement 30 Measures of diagnostic test accuracy 31 Receiver Operating Characteristic (ROC) curve Bibliography Index
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