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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.
- Sales Rank: #178783 in Books
- Published on: 2005-03-16
- Original language: English
- Number of items: 1
- Dimensions: 9.48" h x 1.19" w x 7.46" l, 2.56 pounds
- Binding: Hardcover
- 672 pages
Review
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 . Introduction. Estimating . Testing Hypotheses Concerning . Key Formulas. Statistical Computing. Problems. 7. STATISTICAL INFERENCE: PROCEDURES FOR ( 1- 2) Introduction. Statistical Inference Concerning ( 1- 2). 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.
About the Author
Ralph D'Agostino, Sr. is Professor of Mathematics, Statistics, and Public Health at Boston University. He is a respected and widely published statistician with over 30 years of experience in running clinical trials and epidemiological research. He is a Senior Editor of Statistics in Medicine, Associate Editor of the American Journal of Epidemiology, on the editorial board of the Journal of Hypertension, and Fellow of the American Statistical Association and Epidemiologic Section of the American Heart Association. He is Co-Principal Investigator and Director of Data Management and Statistical Analysis for the Framingham Heart Study (a study collecting data on three generations that has been establishing the relation of risk factors that contribute to cardiovascular disease for over 50 years). He is also the Executive Director of Biometrics and Data Management for the Harvard Clinical Research Institute. He has been a consultant to the Food and Drug Administration since 1974, and has served on a number of drug and devices advisory committees. His interests are in biostatistical methods, robust procedures, longitudinal data analysis and multivariate data analysis. Dr. D'Agostino has received numerous awards, including the Food and Drug Administration Commissioner's Special Citation in 1981 and 1995.). He is co-author of five books in various fields of statistical methodology.
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 has won 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.
Most helpful customer reviews
40 of 42 people found the following review helpful.
great introductory book covering all the key topics
By Michael R. Chernick
In contrast to the first reviewer I loved this book! It should be pointed that I am reviewing this first official edition in hard cover with a CD-ROM while the first reviewer reviewed a preliminary edition in paperback with no CD and published in 2005 whereas this text has a 2006 copyright date.
The authors are all statistics professors at Boston University with Ralph D'Agostino Sr. the most well-known. They have a great deal of experience as teachers researchers and consultants and it shows in the material and the clarity of presentation. Also to their credit, they provide numerous worked out examples in the text and plenty of exericises at the end of the chapters that can be given as homework problems. The other reviewer complained about not having any answers given in the back of the book. I question how valuable that would be. The in-text exercises and the clear exposition in the text , along with an instructor, should be enough for the students to gain enough confidence to try the homework problems with some confidence that they are doing them right! Also the instructor should have a solutions manual to work from or should put one together to go over the homework in class and/or in tutorial sections. Sometime students can waste a lot of time trying to force their solution to match the answer in the back of the book. What if there is a error or typo in the book's answer?
I like the way the authors present the material and the fact that they illustrate the methodology with real examples and SAS software. Many of the analyses are done using the Framingham Heart Study data which is a well designed cohort study with thus far a long 50 year follow-up on the surviving original cohorts. We benefit from the authors' intimate knowledge of this data snd their ability to illustrate a variety of biostatistical methods using it. There is also an interesting follow-up study that was conducted with the offspring of the original Framington cohorts that is discussed in the book.
The book starts with a motivating chapter. I did a similar thing in my introductory book for health science majors. Students in the health sciences are not very interested in math and may have a preconceived notion that statistics is boring mathematics and not relevant to their work. These ideas are very much off base and a good introductory chapter can dispell these notions. The next three chapters are standard to almost any first statistics course starting with ways to summarize data for description. This includes a section on statistical computing snd an introduction to the Framingham study and its data.
Next an introductory chapter on probability is given that includes the basics and some important combinatorial ideas needed to understand the binomial distribution. Then the binomial and normal distributions are presented. This chapter and all subsequent chapters have a section on statistical computing using SAS. Appendix A provides the basic components of SAS that are needed to run analyses in SAS along with sample code and sample output.
Next Chapter 4, as part of a logical progression, covers sampling distributions and the central limit theorem. Chapter 5 covers inference for a single sample mean and includes discussion of power and precision in the important practical problem of deciding how many subjects are needed to draw proper inferences from the trial. This deserves mention because many elementary statistics books avoid this topic and yet it is always one of the first questions a statistician is faced with when he is designing a clinical trial.
Chapter 6 does essentially the same thing as 5 but for two sample problems where the difference of two means is often the key parameter. In the two sample problem there is the added complication of whether or not the two population variances are equal and/or the two sample sizes are equal. Methods are available for all of these situations. Also matched pair designs involve two correlated populations of equal size and can often improve precision over designs that use independent populations to compute the mean difference. Such analyses often come up in clinical trials when baseline and final values are compared for the same subject and for pre-intervention and post-intervention tests in educational trianing studies. Again power and precision the SAS procedures and the analysis of the Framingham Heart Study are given.
The remaining chapters are Categorical Data (Chapter 7) Comparing risks in two populations (Chapter 8), Analysis of Variance (Chapter 9), Correlation and Regression (Chapter 10), Logistic Regression (Chapter 11), Nonparametric Tests (Chapter 12), and Introduction to Survival Analysis (Chapter 13). These are important chapters commonly included in introductory biostatistics texts but chapters such as 8, 10 and 12 are often not included in a general introductory statistics course.
In addition to Appendix A on SAS, Appendix B provides the traditional statistical tables but also shows why the are no longer really necessary since all these table entries can be generated using software. In this case the authors demonstrate it with SAS. And finally, Appendix C provides additional information and data documentation on the Framingham Heart Study.
This text would make a great course text for medical students and health science majors and could even be used as a first biostatistics course for either masters or PhD level statistics students who are interested in biostatistics. It also makes a good reference book for statisticians and biostatisticians.
0 of 0 people found the following review helpful.
Five Stars
By Heath
Great condition
9 of 9 people found the following review helpful.
Introductory Applied Biostatistics
By Tony
This is a great textbook for an introduction to biostatistics. My "required" textbook for my Introduction to Biostatistical Methods was way to technical for me to understand. I got this book to translate and really learn the basics (I have little mathmatics background). Its geared to those who may not have had any statistics backgrounds and easy to read.
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