Introduction to Biostatistics using R (Team-IRA)

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
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