Outline: 1. Study Design-The Basics Hyun Ja Lim and Raymond Hoffmann, Medical College of Wisconsin 1. Introduction 2. Experimental Studies 2.1 Randomized controlled studies 2.2 Historically controlled studies 2.3 Crossover studies 2.4 Factorial designs 2.5 Cluster or group allocation designs 3. Randomization 3.1 Complete or simple randomization 3.2 Block randomization 3.3 Stratified randomization 4. Blinding/Masking 5. Biases 6. Analyses 6.1 Compliance 6.2 Intention-to-treat (ITT) analysis 6.3 As received and per-protocol (PP) analysis 6.4 Subgroup analysis 6.5 Exploratory analyses 7. Study Interpretation 2. Observational Study Design Raymond Hoffmann and Hyun Ja Lim, Medical College of Wisconsin 1. Introduction 2. Cohort Studies 3. Prospective Cohort Studies and Retrospective Cohort Studies 4. Case-Control Studies 4.1 Odds Ratios 4.2 Choice of Controls 4.2 Case-Control Genetic Association Studies 4.3 Matching and Case-Control Studies 4.4 Biases in Case-Control Studies 4.5 Cross-Sectional Studies 5. Outcomes 6. More on Odds Ratios and Relative Risks 6.1 Relative Risks 6.2 Odds Ratios 7. Summary 3. Descriptive Statistics Todd Nick, Cincinnati Children's Hospital 1. Types of Data 2. Measures of location and spread 3. Normal distribution 4. Distribution of a mean 5. Distribution of a variance (including degrees of freedom) 6. Distribution of a proportion 4. Basic Principles of Statistical Inference Wanzhu Tu, Indiana University School of Medicine 1. Introduction 2. Parameter Estimation 2.1 Point Estimation 2.2 Confidence Interval Estimation 2.2.1 Large Sample Confidence Interval for the Mean 2.2.2 Student t-distribution 2.2.3 Small Sample Confidence Interval for the Mean 2.2.4 Simultaneous Inference: Bonferroni's Multiplicity Adjustment 2.2.5 Confidence Interval for the Variance 2.2.6 One-Sided Confidence Intervals 3. Hypothesis Testing 3.1 Understanding Hypothesis Testing 3.2 One sample t test 3.3 An alternaive Decision Rule: P-value 3.4 Errors, Power, and Sample Size 3.5 Statistical Significance and Practical Significance 5. Statistical Inference on Categorical Variables Susan Perkins, Indiana University School of Medicine 1. Introduction 1.1 What is Categorical Data? 1.2 Categorical Data Distributions 1.3 General Notation 1.4 Statistical Analysis Using Categorical Data 2. The Binomial Distribution and the Normal Approximation to the Binomial Distribution 2.1 The Binomial Experiment 2.2 The Binomial Distribution 2.3 The Normal Approximation to the Binomial 3. Estimation and Testing of Single Proportions/Two Proportions 3.1 Estimation of a Single Proportion or the Difference Between Two Proportions 3.2 Hypothesis Testing with a Single Proportion or the Difference Between Two Proportions 3.3 Assumptions 4. Tests of Association 4.1 2x2 Tables 4.2 RxC Tables 4.3 Relationship Between Tests of Independence and Homogeneity 4.4 Fisher's Exact Test 5. McNemar's Test 6. Sample Size Estimation 7. Discussion 6. Development and Evaluation of Classifiers Todd A. Alonzo, University of Southern California, and Margaret Sullivan Pepe, Fred Hutchinson Cancer Research Center and University of Washington 1. Introduction 2. Measures of Classification Accuracy 2.1 True and False Positive Fractions 2.2 Predictive Values 2.3 Diagnostic Likelihood Ratios 2.4 ROC Curves 2.5 Selecting a Measure of Accuracy 3. Basics of Study Design 3.1 Case-control versus Cohort Designs 3.2 Paired versus Unpaired Designs 3.3 Blinding 3.4 Avoiding Bias 3.5 Factors Affecting Test Performance 4. Estimating Performance from Data 4.1 Single binary test 4.2 Comparison of TPF and FPF for two binary tests 4.2.1 Unpaired