INTRODUCTORY APPLIED BIOSTATISTICS provides a solid and engaging background for students learning to apply and appropriately interpret statistical applications in the medical and public health fields. The many examples drawn directly from the authors' remarkable clinical experiences with applied biostatistics make this text relevant, practical, and interesting for students. This flexible textbook encourages students to master application techniques by hand before moving on to computer applications, with SAS programming code and output for each technique covered in every chapter. The majority of the textbook addresses methods for statistical inference, including one- and two-sample tests for means and proportions, analysis of variance techniques, correlation, and regression analysis. For each topic, the book addresses methodology, including assumptions, statistical formulas, and appropriate interpretation of results.

### Table of Contents

1. INTRODUCTION.

2. MOTIVATION.

Introduction. Vocabulary. Population Parameters. Sampling and Sample Statistics. Statistical Inference.

3. SUMMARIZING DATA.

Introduction. Background. Descriptive Statistics and Graphical Methods. Key Formulas. Statistical Computing. Problems.

4. PROBABILITY.

Introduction. Background. First Principles. Combinations and Permutations. The Binomial Distribution. The Normal Distribution. Key Formulas. Applications Using SAS. Problems.

5. SAMPLING DISTRIBUTIONS.

Introduction. Background. The Central Limit Theorem. Key Formulas. Applications Using SAS. Problems.

6. STATISTICAL INFERENCE: PROCEDURES FOR µ.

Introduction. Estimating µ. Testing Hypotheses Concerning µ. Key Formulas. Statistical Computing. Problems.

7. STATISTICAL INFERENCE: PROCEDURES FOR (µ1-µ2)

Introduction. Statistical Inference Concerning (µ1-µ2). Power and Samples Size Determination. Key Formulas. Statistical Computing. Problems.

8. CATEGORICAL DATA.

Introduction. Statistical Inference Concerning p. Cross-tabulation Tables. Diagnostic Tests: Sensitivity and Specificity. Statistical Inference Concerning (p1-p2). Chi-Square Tests. Precision, Power and Sample Size Determination. Key Formulas. Statistical Computing. Problems.

9. COMPARING RISKS IN TWO POPULATIONS.

Introduction. Effect Measures. Confidence Intervals for Effect Measures. The Chi-Square Test of Homogeneity. Fisher's Exact Test. Cox-Mantel-Haenzel Method. Precision, Power and Sample Size Determination. Key Formulas. Statistical Computing. Problems.

10. ANALYSIS OF VARIANCE.

Introduction. Background Logic. Notation and Examples. Fixed vs. Random Effects Models. Evaluating Treatment Effects. Multiple Comparisons. Repeated Measures Analysis of Variance. Key Formulas. Statistical Computing. Problems.

11. CORRELATION AND REGRESSION.

Introduction. Correlation Analysis. Simple Linear Regression. Multiple Regression Analysis. Logistic Regression Analysis. Key Formulas. Statistical Computing. Problems.

12. LOGISTIC REGRESSION ANALYSIS.

Introduction. The Logistic Model. Statistical Inference for Simple Logistic Regression. Multiple Logistic Regression. ROC Area. Key Formulas. Statistical Computing. Problems.

13. NONPARAMETRIC TESTS.

Introduction. The Sign Test (Two Dependent Samples Test). The Wilcoxon Signed-Rank Test (Two Dependent Samples). The Wilcoxon Rank Sum Test (Two Independent Samples). The Kruskal-Wallis Test (k Independent Samples). Spearman Correlation (Correlation between Variables). Key Formulas. Statistical Computing. Problems.

14. INTRODUCTION TO SURVIVAL ANALYSIS.

Introduction. Incomplete Follow-Up. Time to Event. Survival Analysis Techniques.

Appendix A: Introduction to Statistical Computing Using SAS.

Introduction to SAS. The Data Step.

Appendix B. Statistical Tables.

Statistical Tables. SAS Programs used to generate table entries.