Introductory Applied Biostatistics / Edition 2 available in Paperback
- Pub. Date:
- CENGAGE Learning Custom Publishing
INTRODUCTORY APPLIED BIOSTATISTICS (WITH CD-ROM) explores statistical applications in the medical and public health fields. Examples drawn directly from the authors' clinical experiences with applied biostatistics make this text both practical and applicable. You'll master application techniques by hand before moving on to computer applications, with SAS programming code and output for each technique covered in every chapter. For each topic, the book addresses methodology, including assumptions, statistical formulas, and appropriate interpretation of results. This book is a must-have for every student preparing for a statistical career in a healthcare field!
|Publisher:||CENGAGE Learning Custom Publishing|
|Product dimensions:||6.00(w) x 1.25(h) x 9.00(d)|
About the Author
Lisa Sullivan is an Associate Professor of Biostatistics at the School of Public Health, Associate Professor of Mathematics and Statistics at Boston University, and Assistant Dean for Undergraduate Education in Public Health at Boston University. She received both her M.A. and Ph.D. from Boston University. She haswon numerous awards for excellence in teaching and her research interests include applied biostatistics, longitudinal data analysis, design and analysis of clinical trials, and hierarchical modeling. She spends the majority of her time in the Boston University Statistics and Consulting Unit working on the Framingham Heart Study. Her recent research has focused on developing health risk appraisal functions to quantify individuals' risks of developing cardiovascular disease. She has published dozens of articles in prestigious periodicals such as the JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, NEW ENGLAND JOURNAL OF MEDICINE, and STATISTICS IN MEDICINE. Outside of work, Lisa enjoys running and cooking.
Alexa Beiser is Professor of Biostatistics in the School of Public Health at Boston University. She received her M.A. from University of California at San Diego, and her Ph.D. from Boston University. Her research interests include clinical trials methodology, statistical computing, and survival analysis. Dr. Beiser joined the Framingham Study in 1994 after spending many years collaborating on a variety of pediatric research projects. She is primarily involved in the investigation of risk factors for stroke, dementia, and Alzheimer's Disease using data collected as part of the Framingham Study. Dr. Beiser's foremost methodological interest is in estimation of lifetime risk of disease. Dr. Beiser has published articles in the NEW ENGLAND JOURNAL OF MEDICINE, the JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, STATISTICS IN MEDICINE, STROKE and NEUROLOGY. She enjoys reading, traveling and spending time with her four children.
Table of Contents1. 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 TreatmentEffects. 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.