Topics in Biostatistics

Outline:1. Study Design-The BasicsHyun Ja Lim and Raymond Hoffmann, Medical College of Wisconsin1. Introduction2. Experimental Studies2.1 Randomized controlled studies2.2 Historically controlled studies2.3 Crossover studies2.4 Factorial designs2.5 Cluster or group allocation designs3. Randomization3.1 Complete or simple randomization3.2 Block randomization3.3 Stratified randomization4. Blinding/Masking5. Biases6. Analyses6.1 Compliance6.2 Intention-to-treat (ITT) analysis6.3 As received and per-protocol (PP) analysis6.4 Subgroup analysis6.5 Exploratory analyses7. Study Interpretation2. Observational Study DesignRaymond Hoffmann and Hyun Ja Lim, Medical College of Wisconsin1. Introduction2. Cohort Studies3. Prospective Cohort Studies and Retrospective Cohort Studies4. Case-Control Studies4.1 Odds Ratios4.2 Choice of Controls4.2 Case-Control Genetic Association Studies4.3 Matching and Case-Control Studies4.4 Biases in Case-Control Studies4.5 Cross-Sectional Studies5. Outcomes6. More on Odds Ratios and Relative Risks6.1 Relative Risks6.2 Odds Ratios7. Summary3. Descriptive StatisticsTodd Nick, Cincinnati Children’s Hospital1. Types of Data2. Measures of location and spread3. Normal distribution4. Distribution of a mean5. Distribution of a variance (including degrees of freedom)6. Distribution of a proportion4. Basic Principles of Statistical InferenceWanzhu Tu, Indiana University School of Medicine1. Introduction2. Parameter Estimation2.1 Point Estimation2.2 Confidence Interval Estimation2.2.1 Large Sample Confidence Interval for the Mean2.2.2 Student t-distribution2.2.3 Small Sample Confidence Interval for the Mean2.2.4 Simultaneous Inference: Bonferroni’s MultiplicityAdjustment2.2.5 Confidence Interval for the Variance2.2.6 One-Sided Confidence Intervals3. Hypothesis Testing3.1 Understanding Hypothesis Testing3.2 One sample t test3.3 An alternaive Decision Rule: P-value3.4 Errors, Power, and Sample Size3.5 Statistical Significance and Practical Significance5. Statistical Inference on Categorical VariablesSusan Perkins, Indiana University School of Medicine1. Introduction1.1 What is Categorical Data?1.2 Categorical Data Distributions1.3 General Notation1.4 Statistical Analysis Using Categorical Data2. The Binomial Distribution and the Normal Approximation to theBinomialDistribution2.1 The Binomial Experiment2.2 The Binomial Distribution2.3 The Normal Approximation to the Binomial3. Estimation and Testing of Single Proportions/Two Proportions3.1 Estimation of a Single Proportion or the Difference Between TwoProportions3.2 Hypothesis Testing with a Single Proportion or the DifferenceBetween Two Proportions3.3 Assumptions4. Tests of Association4.1 2x2 Tables4.2 RxC Tables4.3 Relationship Between Tests of Independence and Homogeneity4.4 Fisher’s Exact Test5. McNemar’s Test6. Sample Size Estimation7. Discussion6. Development and Evaluation of ClassifiersTodd A. Alonzo, University of Southern California, and MargaretSullivan Pepe, Fred Hutchinson Cancer Research Center and University ofWashington1. Introduction2. Measures of Classification Accuracy2.1 True and False Positive Fractions2.2 Predictive Values2.3 Diagnostic Likelihood Ratios2.4 ROC Curves2.5 Selecting a Measure of Accuracy3. Basics of Study Design3.1 Case-control versus Cohort Designs3.2 Paired versus Unpaired Designs3.3 Blinding3.4 Avoiding Bias3.5 Factors Affecting Test Performance4. Estimating Performance from Data4.1 Single binary test4.2 Comparison of TPF and FPF for two binary tests4.2.1 Unpaired