Efnisyfirlit

• Cover
• Title Page
• Copyright
• Introduction
• About This Book
• Foolish Assumptions
• Icons Used in This Book
• Beyond the Book
• Where to Go from Here
• Part 1: Getting Started with Biostatistics
• Chapter 1: Biostatistics 101
• Brushing Up on Math and Stats Basics
• Doing Calculations with the Greatest of Ease
• Concentrating on Epidemiologic Research
• Drawing Conclusions from Your Data
• A Matter of Life and Death: Working with Survival Data
• Getting to Know Statistical Distributions
• Figuring Out How Many Participants You Need
• Chapter 2: Overcoming Mathophobia: Reading and Understanding Mathematical Expressions
• Breaking Down the Basics of Mathematical Formulas
• Focusing on Operations Found in Formulas
• Counting on Collections of Numbers
• Chapter 3: Getting Statistical: A Short Review of Basic Statistics
• Taking a Chance on Probability
• Some Random Thoughts about Randomness
• Selecting Samples from Populations
• Introducing Statistical Inference
• Honing In on Hypothesis Testing
• Going Outside the Norm with Nonparametric Statistics
• Part 2: Examining Tools and Processes
• Chapter 4: Counting on Statistical Software
• Considering the Evolution of Statistical Software
• Comparing Commercial to Open-Source Software
• Checking Out Commercial Software
• Focusing on Open-Source and Free Software
• Choosing Between Code-based and Non–Code-Based Methods
• Storing Data in the Cloud
• Chapter 5: Conducting Clinical Research
• Designing a Clinical Trial
• Carrying Out a Clinical Trial
• Analyzing Your Data
• Chapter 6: Taking All Kinds of Samples
• Making Forgivable (and Non-Forgivable) Errors
• Framing Your Sample
• Sampling for Success
• Chapter 7: Having Designs on Study Design
• Presenting the Study Design Hierarchy
• Climbing the Evidence Pyramid
• Part 3: Getting Down and Dirty with Data
• Chapter 8: Getting Your Data into the Computer
• Looking at Levels of Measurement
• Classifying and Recording Different Kinds of Data
• Checking Your Entered Data for Errors
• Creating a File that Describes Your Data File
• Chapter 9: Summarizing and Graphing Your Data
• Summarizing and Graphing Categorical Data
• Summarizing Numerical Data
• Structuring Numerical Summaries into Descriptive Tables
• Graphing Numerical Data
• Chapter 10: Having Confidence in Your Results
• Feeling Confident about Confidence Interval Basics
• Calculating Confidence Intervals
• Relating Confidence Intervals and Significance Testing
• Part 4: Comparing Groups
• Chapter 11: Comparing Average Values between Groups
• Grasping Why Different Situations Need Different Tests
• Using Statistical Tests for Comparing Averages
• Estimating the Sample Size You Need for Comparing Averages
• Chapter 12: Comparing Proportions and Analyzing Cross-Tabulations
• Examining Two Variables with the Pearson Chi-Square Test
• Focusing on the Fisher Exact Test
• Calculating Power and Sample Size for Chi-Square and Fisher Exact Tests
• Chapter 13: Taking a Closer Look at Fourfold Tables
• Focusing on the Fundamentals of Fourfold Tables
• Choosing the Correct Sampling Strategy
• Producing Fourfold Tables in a Variety of Situations
• Chapter 14: Analyzing Incidence and Prevalence Rates in Epidemiologic Data
• Understanding Incidence and Prevalence
• Analyzing Incidence Rates
• Estimating the Required Sample Size
• Part 5: Looking for Relationships with Correlation and Regression
• Chapter 15: Introducing Correlation and Regression
• Correlation: Estimating How Strongly Two Variables Are Associated
• Regression: Discovering the Equation that Connects the Variables
• Chapter 16: Getting Straight Talk on Straight-Line Regression
• Knowing When to Use Straight-Line Regression
• Understanding the Basics of Straight-Line Regression
• Running a Straight-Line Regression
• Interpreting the Output of Straight-Line Regression
• Recognizing What Can Go Wrong with Straight-Line Regression
• Calculating the Sample Size You Need
• Chapter 17: More of a Good Thing: Multiple Regression
• Understanding the Basics of Multiple Regression
• Executing a Multiple Regression Analysis in Software
• Interpreting the Output of a Multiple Regression Analysis
• Watching Out for Special Situations that Arise in Multiple Regression
• Calculating How Many Participants You Need
• Chapter 18: A Yes-or-No Proposition: Logistic Regression
• Using Logistic Regression
• Understanding the Basics of Logistic Regression
• Fitting a function with an S shape to your data
• Running a Logistic Regression Model with Software
• Interpreting the Output of Logistic Regression
• Heads Up: Knowing What Can Go Wrong with Logistic Regression
• Figuring Out the Sample Size You Need for Logistic Regression
• Chapter 19: Other Useful Kinds of Regression
• Analyzing Counts and Rates with Poisson Regression
• Anything Goes with Nonlinear Regression
• Smoothing Nonparametric Data with LOWESS
• Chapter 20: Getting the Hint from Epidemiologic Inference
• Staying Clearheaded about Confounding
• Understanding Interaction (Effect Modification)
• Getting Casual about Cause
• Part 6: Analyzing Survival Data
• Chapter 21: Summarizing and Graphing Survival Data
• Understanding the Basics of Survival Data
• Looking at the Life-Table Method
• Heeding a Few Guidelines for Life-Tables and the Kaplan-Meier Method
• Chapter 22: Comparing Survival Times
• Comparing Survival between Two Groups with the Log-Rank Test
• Considering More Complicated Comparisons
• Estimating the Sample Size Needed for Survival Comparisons
• Chapter 23: Survival Regression
• Knowing When to Use Survival Regression
• Grasping the Concepts behind Survival Regression
• Executing a Survival Regression
• Interpreting the Output of a Survival Regression
• How Long Have I Got, Doc? Constructing Prognosis Curves
• Estimating the Required Sample Size for a Survival Regression
• Part 7: The Part of Tens
• Chapter 24: Ten Distributions Worth Knowing
• The Uniform Distribution
• The Normal Distribution
• The Log-Normal Distribution
• The Binomial Distribution
• The Poisson Distribution
• The Exponential Distribution
• The Weibull Distribution
• The Student t Distribution
• The Chi-Square Distribution
• The Fisher F Distribution
• Chapter 25: Ten Easy Ways to Estimate How Many Participants You Need
• Comparing Means between Two Groups
• Comparing Means among Three, Four, or Five Groups
• Comparing Paired Values
• Comparing Proportions between Two Groups
• Testing for a Significant Correlation
• Comparing Survival between Two Groups
• Scaling from 80 Percent to Some Other Power
• Scaling from 0.05 to Some Other Alpha Level
• Adjusting for Unequal Group Sizes
• Allowing for Attrition
• Index
• About the Authors
• Connect with Dummies
• End User License Agreement