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• Title Page
• Copyright Page
• Table of Contents
• Introduction
• About This Book
• Conventions Used in This Book
• What You’re Not to Read
• Foolish Assumptions
• How This Book Is Organized
• Part I: Beginning with Biostatistics Basics
• Part II: Getting Down and Dirty with Data
• Part III: Comparing Groups
• Part IV: Looking for Relationships with Correlation and Regression
• Part V: Analyzing Survival Data
• Part VI: The Part of Tens
• Icons Used in This Book
• Where to Go from Here
• Part I: Beginning with Biostatistics Basics
• Chapter 1: Biostatistics 101
• Brushing Up on Math and Stats Basics
• Doing Calculations with the Greatest of Ease
• Concentrating on Clinical Research
• Drawing Conclusions from Your Data
• Statistical estimation theory
• Statistical decision theory
• A Matter of Life and Death: Working with Survival Data
• Figuring Out How Many Subjects You Need
• Getting to Know Statistical Distributions
• Chapter 2: Overcoming Mathophobia: Reading and Understanding Mathematical Expressions
• Breaking Down the Basics of Mathematical Formulas
• Displaying formulas in different ways
• Checking out the building blocks of formulas
• Focusing on Operations Found in Formulas
• Basic mathematical operations
• Powers, roots, and logarithms
• Factorials and absolute values
• Functions
• Simple and complicated formulas
• Equations
• Counting on Collections of Numbers
• One-dimensional arrays
• Higher-dimensional arrays
• Arrays in formulas
• Sums and products of the elements of an array
• Chapter 3: Getting Statistical: A Short Review of Basic Statistics
• Taking a Chance on Probability
• Thinking of probability as a number
• Following a few basic rules
• Comparing odds versus probability
• Some Random Thoughts about Randomness
• Picking Samples from Populations
• Recognizing that sampling isn’t perfect
• Digging into probability distributions
• Introducing Statistical Inference
• Statistical estimation theory
• Statistical decision theory
• Homing In on Hypothesis Testing
• Getting the language down
• Testing for significance
• Understanding the meaning of “p value” as the result of a test
• Examining Type I and Type II errors
• Grasping the power of a test
• Going Outside the Norm with Nonparametric Statistics
• Chapter 4: Counting on Statistical Software
• Desk Job: Personal Computer Software
• Checking out commercial software
• Focusing on free software
• On the Go: Calculators and Mobile Devices
• Scientific and programmable calculators
• Mobile devices
• Gone Surfin’: Web-Based Software
• On Paper: Printed Calculators
• Chapter 5: Conducting Clinical Research
• Designing a Clinical Study
• Identifying aims, objectives, hypotheses, and variables
• Deciding who will be in the study
• Choosing the structure of the study
• Using randomization
• Selecting the analyses to use
• Defining analytical populations
• Determining how many subjects to enroll
• Putting together the protocol
• Carrying Out a Clinical Study
• Protecting your subjects
• Collecting and validating data
• Analyzing Your Data
• Dealing with missing data
• Handling multiplicity
• Incorporating interim analyses
• Chapter 6: Looking at Clinical Trials and Drug Development
• Not Ready for Human Consumption: Doing Preclinical Studies
• Testing on People during Clinical Trialsto Check a Drug’s Safety and Efficacy
• Phase I: Determining the maximum tolerated dose
• Phase II: Finding out about the drug’s performance
• Phase III: Proving that the drug works
• Phase IV: Keeping an eye on the marketed drug
• Holding Other Kinds of Clinical Trials
• Pharmacokinetics and pharmacodynamics (PK/PD studies)
• Bioequivalence studies
• Thorough QT studies
• Part II: Getting Down and Dirty with Data
• Chapter 7: Getting Your Data into the Computer
• Looking at Levels of Measurement
• Classifying and Recording Different Kinds of Data
• Dealing with free-text data
• Assigning subject identification (ID) numbers
• Organizing name and address data
• Collecting categorical data
• Recording numerical data
• Entering date and time data
• Checking Your Entered Data for Errors
• Creating a File that Describes Your Data File
• Chapter 8: Summarizing and Graphing Your Data
• Summarizing and Graphing Categorical Data
• Summarizing Numerical Data
• Locating the center of your data
• Describing the spread of your data
• Showing the symmetry and shape of the distribution
• Structuring Numerical Summaries into Descriptive Tables
• Graphing Numerical Data
• Showing the distribution with histograms
• Summarizing grouped data withbars, boxes, and whiskers
• Depicting the relationships between numerical variables with other graphs
• Chapter 9: Aiming for Accuracy and Precision
• Beginning with the Basics of Accuracy and Precision
• Getting to know sample statistics and population parameters
• Understanding accuracy and precision in terms of the sampling distribution
• Thinking of measurement as a kind of sampling
• Expressing errors in terms of accuracy and precision
• Improving Accuracy and Precision
• Enhancing sampling accuracy
• Getting more accurate measurements
• Improving sampling precision
• Increasing the precision of your measurements
• Calculating Standard Errors for Different Sample Statistics
• A mean
• A proportion
• Event counts and rates
• A regression coefficient
• Chapter 10: Having Confidence in Your Results
• Feeling Confident about Confidence Interval Basics
• Defining confidence intervals
• Looking at confidence levels
• Taking sides with confidence intervals
• Calculating Confidence Intervals
• Before you begin: Formulas for confidence limits in large samples
• The confidence interval around a mean
• The confidence interval around a proportion
• The confidence interval around an event count or rate
• The confidence interval around a regression coefficient
• Relating Confidence Intervals and Significance Testing
• Chapter 11: Fuzzy In Equals Fuzzy Out: Pushing Imprecision through a Formula
• Understanding the Concept of Error Propagation
• Using Simple Error Propagation Formulas for Simple Expressions
• Adding or subtracting a constantdoesn’t change the SE
• Multiplying (or dividing) by a constant multiplies (or divides) the SE by the same amount
• For sums and differences: Add the squares of SEs together
• For averages: The square root law takes over
• For products and ratios: Squares of relative SEs are added together
• For powers and roots: Multiply the relative SE by the power
• Handling More Complicated Expressions
• Using the simple rules consecutively
• Checking out an online calculator
• Simulating error propagation — easy, accurate, and versatile
• Part III: Comparing Groups
• Chapter 12: Comparing Average Values between Groups
• Knowing That Different Situations Need Different Tests
• Comparing the mean of a group of numbers to a hypothesized value
• Comparing two groups of numbers
• Comparing three or more groups of numbers
• Analyzing data grouped on several different variables
• Adjusting for a “nuisance variable” when comparing numbers
• Comparing sets of matched numbers
• Comparing within-group changes between groups
• Trying the Tests Used for Comparing Averages
• Surveying Student t tests
• Assessing the ANOVA
• Running Student t tests and ANOVAs from summary data
• Running nonparametric tests
• Estimating the Sample Size You Need for Comparing Averages
• Simple formulas
• Software and web pages
• A sample-size nomogram
• Chapter 13: Comparing Proportions and Analyzing Cross-Tabulations
• Examining Two Variables with the Pearson Chi-Square Test
• Understanding how the chi-square test works
• Pointing out the pros and cons of the chi-square test
• Modifying the chi-square test: The Yates continuity correction
• Focusing on the Fisher Exact Test
• Understanding how the Fisher Exact test works
• Noting the pros and cons of the Fisher Exact test
• Calculating Power and Sample Size for Chi-Square and Fisher Exact Tests
• Analyzing Ordinal Categorical Data with the Kendall Test
• Studying Stratified Data with the Mantel-Haenszel Chi-Square Test
• Chapter 14: Taking a Closer Look at Fourfold Tables
• Focusing on the Fundamentals of Fourfold Tables
• Choosing the Right Sampling Strategy
• Producing Fourfold Tables in a Variety of Situations
• Describing the association between two binary variables
• Assessing risk factors
• Evaluating diagnostic procedures
• Investigating treatments
• Looking at inter- and intra-rater reliability
• Chapter 15: Analyzing Incidence and Prevalence Rates in Epidemiologic Data
• Understanding Incidence and Prevalence
• Prevalence: The fraction of a population with a particular condition
• Incidence: Counting new cases
• Understanding how incidence and prevalence are related
• Analyzing Incidence Rates
• Expressing the precision of an incidence rate
• Comparing incidences with the rate ratio
• Calculating confidence intervals for a rate ratio
• Comparing two event rates
• Comparing two event counts with identical exposure
• Estimating the Required Sample Size
• Chapter 16: Feeling Noninferior (Or Equivalent)
• Understanding the Absence of an Effect
• Defining the effect size: How different are the groups?
• Defining an important effect size: How close is close enough?
• Recognizing effects: Can you spot a difference if there really is one?
• Proving Equivalence and Noninferiority
• Using significance tests
• Using confidence intervals
• Some precautions about noninferiority testing
• Part IV: Looking for Relationships with Correlation and Regression
• Chapter 17: Introducing Correlation and Regression
• Correlation: How Strongly Are Two Variables Associated?
• Lining up the Pearson correlation coefficient
• Analyzing correlation coefficients
• Regression: What Equation Connects the Variables?
• Understanding the purpose of regression analysis
• Talking about terminology and mathematical notation
• Classifying different kinds of regression
• Chapter 18: 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
• Taking a few basic steps
• Walking through an example
• Interpreting the Output of Straight-Line Regression
• Seeing what you told the program to do
• Looking at residuals
• Making your way through the regression table
• Wrapping up with measures of goodness-of-fit
• Scientific fortune-telling with the prediction formula
• Recognizing What Can Go Wrong with Straight-Line Regression
• Figuring Out the Sample Size You Need
• Chapter 19: More of a Good Thing: Multiple Regression
• Understanding the Basics of Multiple Regression
• Defining a few important terms
• Knowing when to use multiple regression
• Being aware of how the calculations work
• Running Multiple Regression Software
• Preparing categorical variables
• Recoding categorical variables as numerical
• Creating scatter plots before you jumpinto your multiple regression
• Taking a few steps with your software
• Interpreting the Output of a Multiple Regression
• Examining typical output from most programs
• Checking out optional output available from some programs
• Deciding whether your data is suitable for regression analysis
• Determining how well the model fits the data
• Watching Out for Special Situations that Arise in Multiple Regression
• Synergy and anti-synergy
• Collinearity and the mystery ofthe disappearing significance
• Figuring How Many Subjects You Need
• Chapter 20: A Yes-or-No Proposition: Logistic Regression
• Using Logistic Regression
• Understanding the Basics of Logistic Regression
• Gathering and graphing your data
• Fitting a function with an S shape to your data
• Handling multiple predictors in your logistic model
• Running a Logistic Regression with Software
• Interpreting the Output of Logistic Regression
• Seeing summary information about the variables
• Assessing the adequacy of the model
• Checking out the table of regression coefficients
• Predicting probabilities with the fitted logistic formula
• Making yes or no predictions
• Heads Up: Knowing What Can Go Wrong with Logistic Regression
• Don’t fit a logistic function to nonlogistic data
• Watch out for collinearity and disappearing significance
• Check for inadvertent reverse-coding of the outcome variable
• Don’t misinterpret odds ratios for numericalpredictors
• Don’t misinterpret odds ratios for categorical predictors
• Beware the complete separation problem
• Figuring Out the Sample Size You Need for Logistic Regression
• Chapter 21: Other Useful Kinds of Regression
• Analyzing Counts and Rates with Poisson Regression
• Introducing the generalized linear model
• Running a Poisson regression
• Interpreting the Poisson regression output
• Discovering other things that Poisson regression can do
• Anything Goes with Nonlinear Regression
• Distinguishing nonlinear regression from other kinds
• Checking out an example from drug research
• Running a nonlinear regression
• Interpreting the output
• Using equivalent functions to fit the parameters you really want
• Smoothing Nonparametric Data with LOWESS
• Running LOWESS
• Adjusting the amount of smoothing
• Part V: Analyzing Survival Data
• Chapter 22: Summarizing and Graphing Survival Data
• Understanding the Basics of Survival Data
• Knowing that survival times are intervals
• Recognizing that survival times aren’t normally distributed
• Considering censoring
• Looking at the Life-Table Method
• Making a life table
• Interpreting a life table
• Graphing hazard rates and survival probabilities from a life table
• Digging Deeper with the Kaplan-Meier Method
• Heeding a Few Guidelines for Life Tables and the Kaplan-Meier Method
• Recording survival times the right way
• Recording censoring information correctly
• Interpreting those strange-looking survivalcurves
• Doing Even More with Survival Data
• Chapter 23: Comparing Survival Times
• Comparing Survival between Two Groups with the Log-Rank Test
• Understanding what the log-rank test is doing
• Running the log-rank test on software
• Looking at the calculations
• Assessing the assumptions
• Considering More Complicated Comparisons
• Coming Up with the Sample Size Needed for Survival Comparisons
• Chapter 24: Survival Regression
• Knowing When to Use Survival Regression
• Explaining the Concepts behind Survival Regression
• The steps of Cox PH regression
• Hazard ratios
• Running a Survival Regression
• Interpreting the Output of a Survival Regression
• Testing the validity of the assumptions
• Checking out the table of regression coefficients
• Homing in on hazard ratios and their confidence intervals
• Assessing goodness-of-fit and predictive ability of the model
• Focusing on baseline survival and hazard functions
• How Long Have I Got, Doc? Constructing Prognosis Curves
• Running the proportional-hazards regression
• Finding h
• Estimating the Required Sample Size for a Survival Regression
• Part VI: The Part of Tens
• Chapter 25: 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 26: Ten Easy Ways to Estimate How Many Subjects 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
• Making Adjustments for Unequal Group Sizes
• Allowing for Attrition
• Index
• EULA