Table of Contents

1. Statistical Methods and Challenges in Epidemiology and Biomedical Research (Ross Prentice)
2. Statistical Inference for Causal Effects, with Emphasis on Applications in Epidemiology and Medical Statistics (Donald Rubin)
3. Epidemiological Study Designs (Kenneth Rothman, Sander Greenland, Timothy Lash)
4. Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data (Stephen Looney and Joseph Hagan)
5. Linear and Nonlinear Regression Methods in Epidemiology and Biostatistics (Charles McCulloch, David Glidden, Stephen Shiboski, Eric Vittinghoff)
6. Logistic Regression (Ed Spitznagel)
7. Count Response Regression Models (Joseph Hilbe and William Greene)
8. Mixed Models (Matthew Gurka and Lloyd Edwards)
9. Survival Analysis (John Klein and Meijie Zhang)
10. A Review of Statistical Analysis for Competing Risks (Melvin Moeschberger, Kevin Tordoff, Nidhi Kochar)
11. Cluster Analysis (Bill Shannon)
12. Factor Analysis and Related Methods (Carol Woods and Michael Edwards)
13. Structural Equations Modeling (Kentaro Hayashi, Peter Bentler, Ke-Hai Yuan)
14. Statistical Modeling in Biomedical Research: Longitudinal Data Analysis (Chengjie Xiong, Kejun Zhu, Kai Yu, Phil Miller)
15. Design and Analysis of Cross-Over Trials (Michael Kenward and Byron Jones)
16. Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners (Madhu Mazumdar and Heejung Bang)
17. Early Phase Clinical Trials: Phase I and II (Feng Gao, Kim Trinkaus, Phil Miller)
18. Definitive Phase III and Phase IV Clinical Trials (Barry Davis and Sarah Baraniuk)
19. Incomplete Data in Epidemiology and Medical Statistics (Susanne Rassler, Donald Rubin, Elizabeth Zell)
20. Meta-Analysis (Ed Spitznagel)
21. The Multiple Comparison Issue in Health Care Research (Lemuel Moye)
22. Power: Establishing the Optimum Sample Size
(Richard Zeller and Yan Yan)
23. Statistical Learning in Medical Data Analysis
(Grace Wahba)
24. Evidence-Based Medicine and Medical Decision Making (Dan Mayer)
25. Estimation of Marginal Regression Models with Multiple Source Predictors (Heather Litman, Nicholas Horton, Bernardo Hernandez, Nan Laird)
26. Difference Equations with Public Health Applications (Asha Kapadia and Lemuel Moye)
27. The Bayesian Approach to Experimental Data Analysis (Bruno Lecoutre)

About the Author

C. R. Rao, born in India is one of this century's foremost statisticians, received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. Rao is currently at Penn State as Eberly Professor of Statistics and Director of the Center for Multivariate Analysis. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.