Introduction to Biostatistics using R (Team-IRA)

✍ Scribed by Mohsen Nady

• Publisher: Arcler Press
• Year: 2022
• Tongue: English
• Leaves: 521
• Category: Library

✦ Synopsis

This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 9–11 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 12–14 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects.

✦ Table of Contents

Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Abbreviations
Preface
Chapter 1 Introduction to Statistics
1.1. The Role of Statistics in Biology
1.2. Research Project Steps
1.3. Sample and Population
1.4. Study Designs
1.5. Data Types
1.6. Examining Different Data Types Using R
Chapter 2 Numerical Data
2.1. Measures of Location for Univariate Numerical Data
2.2. Measures of Spread for Univariate Numerical Data
2.3. Graphical Methods for Univariate Numerical data
2.4. Comparing Two Numerical Variables, Numerical Measures
2.5. Comparing Two Numerical Variables, Graphical Methods
2.6. Comparing One Numerical and One Categorical Variable, Numerical Measures
2.7. Comparing One Numerical and One Categorical Variable, Graphical Methods
Chapter 3 The Normal Distribution
3.1. Introduction to Normal Distribution
3.2. The 68-95-99.7% Rule
3.3. Applying Normal Distribution to Sample Data
3.4. The z Score “Statistical Mile”
3.5. Applying the Normal Distribution to Skewed Data
Chapter 4 Binary and Categorical Data
4.1. Definitions
4.2. Summarizing Categorical Data
4.3. Visualizing Categorical Data
4.4. Comparing Categorical Data Across Two or More Populations, Numerical Measures
4.5. Comparing Categorical Data Across Two or More Populations, Graphical Methods
Chapter 5 Time to Event Data = Survival Data = Failure Time Data
5.1. Introduction
5.2. Numerical Summaries
5.3. Graphical Summaries: Kaplan-Meier Approach
5.4. Using Ratios for Statistical Tests
Chapter 6 Sampling Distribution
6.1. Introduction
6.2. The Sampling Distribution of the Sample Means
6.3. The Sampling Distribution of Sample Proportions
6.4. The Sampling Distribution of Sample Incidence Rates (IRs)
6.5. The Central Limit Theorem (CLT)
Chapter 7 Confidence Intervals
7.1. Introduction
7.2. Confidence Interval (CI) for a Single Population Parameter (Mean, Proportion, Incidence Rate (IR))
7.3. Calculation of Confidence Intervals (CI)
Chapter 8 Confidence Intervals for Comparing Two or More Populations
8.1. Introduction
8.2. Extension of the Central Limit Theorem (CLT)
8.3. Null Values
8.4. Confidence Interval (CI) for Comparing Means Between Two or More Populations, Mean Difference
8.5. Confidence Interval (CI) for Comparing Proportions Between Two or More Populations, Proportion Difference
8.6. Confidence Interval (CI) for Comparing Proportions Between Two or More Populations, Relative Risk (RR) and Odds Ratio (OR)
8.7. Confidence Interval (CI) for Comparing Incidence Rate (IR) Between Two or More Populations, Incidence Rate Ratios (IRRs)
Chapter 9 Hypothesis Testing for Comparing Means
9.1. Introduction to Hypothesis Testing
9.2. Hypothesis Testing for Comparing Means Between Two Populations
9.3. Hypothesis Testing for Comparing Means Between Two Populations, Non-Parametric Tests
Chapter 10 Hypothesis Testing for Proportions and Time to Event Data
10.1. Comparing Proportions Between Two Populations Using Chi-Square Test
10.2. Comparing Proportions Between Two Populations Using Fisher Exact Test
10.3. Comparing Proportions Between Two Populations Using McNemar Test (Paired Data)
10.4. Comparing Time to Event Data Between Two Populations Using Log-Rank Test
Chapter 11 Hypothesis Testing for More Than Two Populations
11.1. The Problem of Multiple Comparisons in Statistical Tests
11.2. Comparing Means Between More Than Two Populations Using Analysis of Variance (ANOVA) Test
11.3. Comparing Means Between More Than Two Populations Using Kruskal-Wallis Test
11.4. Comparing Proportions Between More Than 2 Populations Using Chi-Square Test
11.5. Comparing Proportions Between More Than 2 Populations Using Fisher Exact Test
11.6. Comparing Survival Curves Between More Than Two Populations Using Log-Rank Test
Chapter 12 Simple and Multiple Linear Regression
12.1. An Overview of Simple Regression
12.2. Simple Linear Regression with Categorical Predictor
12.3. Simple Linear Regression with Continuous Predictor
12.4. Multiple Regression
12.5. Evaluating the Regression Model
Chapter 13 Simple and Multiple Logistic Regression
13.1. Simple Logistic Regression with Categorical Predictor
13.2. Simple Logistic Regression with Continuous Predictor
13.3. Multiple Logistic Regression
13.4. Evaluation of the Regression Model
Chapter 14 Simple and Multiple Cox Regression
14.1. Introduction
14.2. Cox Regression with Categorical Predictor
14.3. Cox Regression with Continuous Predictor
14.4. Multiple Cox Regression
14.5. Evaluation of the Cox Model
Bibliography
Index
Back Cover