Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009).

While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas.

· Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

1. Statistical Methods and Challenges in Epidemiology and Biomedical Research (Ross L. Prentice)

2. Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data (Stephen W. Looney, Joseph L. Hagan)

3. Linear and Non-Linear Regression Methods in Epidemiology and Biostatistics (Eric Vittinghoff, Charles E. McCulloch, David V. Glidden, Stephen C. Shiboski)

4. Count Response Regression Models (Joseph M. Hilbe, William H. Greene)

5. Mixed Models (Matthew J. Gurka, Lloyd J. Edwards)

6. Factor Analysis and Related Methods (Carol M. Woods, Michael C. Edwards)

7. Structural Equation Modeling (Kentaro Hayashi, Peter M. Bentler, Ke-Hai Yuan)

8. Statistical Modeling in Biomedical Research: Longitudinal Data Analysis (Chengjie Xiong, Kejun Zhu, Kai Yu, J. Philip Miller)

9. Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners (Madhu Mazumdar, Heejung Bang)

10. Estimation of Marginal Regression Models with Multiple Source Predictors (Heather J. Litman, Nicholas J. Horton, Bernardo Hernández, Nan M. Laird)

11. The Bayesian Approach to Experimental Data Analysis (Bruno Lecoutre)