Buch, Englisch, 368 Seiten, Format (B × H): 159 mm x 236 mm, Gewicht: 707 g
A Derivative of Handbook of Statistics: Epidemiology and Medical Statistics, Vol. 27
Buch, Englisch, 368 Seiten, Format (B × H): 159 mm x 236 mm, Gewicht: 707 g
ISBN: 978-0-444-53737-9
Verlag: Elsevier BV
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.
Zielgruppe
<p>researchers graduate students consultants Also: pharmaceutical companies (theoretical and applied statisticians) statisticians FDA, EPA, etc libraries</p>
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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)
