This new edition of Biostatistics: The Bare Essentials continues the tradition of translating biostatistics in the health sciences literature with clarity and irreverence. Students and practitioners alike applaud Biostatistics as a practical guide that exposes them to every statistical test they are likely to encounter, with careful conceptual explanations and a minimum of algebra. What's New? The previous edition of Bare Essentials presented hierarchical linear modeling, which first appeared in psychology journals and has only recently been described in the medical literature. The 3rd edition also introduced a chapter on testing for equivalence and non-inferiority as well as a chapter with information for getting started with the computer statistics program SPSS. A very positive review of the 3rd edition of the book by Dr. Naomi Vaisrub appeared in JAMA which praised the book but recommended covering topics in epidemiology, so in the 4th edition the authors took her up on it. They've also included an entirely new chapter, called "Measures of Impact," in which they discuss measures of incidence and prevalence, risk, morbidity and fatality rates, and the number needed to treat.
They also delve into the Poisson distribution for doing regressions on count data. Likewise, the reader will find new sections on robust estimators of the mean, the problems of multiple hypothesis testing, bootstrapping and resampling, as well as an expanded section on nonparametric stats. Free of calculations and jargon, Bare Essentials speaks so plainly that you won't need a technical dictionary. The focus is on the concepts, not the math. The objective is to enable you to determine whether the research results are applicable to your own patients. Throughout, you'll find highlights of areas in which researchers misuse or misinterpret statistical tests. The authors have labeled these "C.R.A.P. Detectors" (Convoluted Reasoning and Anti-Intellectual Pomposity), and they help you identify faulty methodology and misuse of statistics.
Section the First: The Nature of Data and Statistics 1. The Basics 2. Looking at the Data: A First look at Graphing Data 3. Describing the Data with Numbers: Measures of Central Tendency and Dispersion 4. The Normal Distribution 5. Probability 6. Elements of Statistical Inference Section the Second: Analysis of Variance 7. Comparing Two Groups: The t-Test 8. More than Two Groups: One-Way ANOVA 9. Factorial ANOVA 10. Two Repeated Observations: The Paired t-Test and Alternatives 11. Repeated-Measures ANOVA 12. Multivariate ANOVA (MANOVA) Section the Third: Regression and Correlation 13. Simple Regression and Correlation 14. Multiple Regression 15. Logistic Regression 16. Advanced Topics in Regression and ANOVA 17. Measuring Change 18. Analysis of Longitudinal Data: Hierarchical Linear Models 19. Principal Components and Factor Analysis: Fooling Around with Factors 20. Path Analysis and Structural Equation Modeling Section the Fourth: Nonparametric Statistics 21. Tests of Significance for Categorical Frequency Data 22. Measures of Association for Categorical Data 23. Tests of Significance for Ranked Data 24. Measures of Association for Ranked Data 25. Life-Table (Survival) Analysis 26. Measures of Impact Section the Fifth: Reprise 27. Equivalence and Non-Inferiority Testing 28. Screwups, Oddballs, and other Vagaries of Science Locating Outliers, Handling Missing Data, and Transformations 29. Putting It All Together 30. Getting Started with SPSS Test Yourself (Being a Compendium of Questions and Answers) Answers to Chapter Exercises References and Further Reading Unabashed Glossary Appendices Index