Logistic Regression.: A Self-Learning Text (Statistics for Biology and Health) - Tapa dura

This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

David G. Kleinbaum is Associate Professor of Biostatistics and Epidemiology at the University of North Carolina School of Public Health and holds a Ph.D. from the University of North Carolina.

Lawrence L. Kupper is Professor of Biostatistics and Epidemiology at the University of North Carolina School of Public Health and holds a Ph.D. from the University of North Carolina. Drs. Kleinbaum and Kupper have worked on the development of applications of statistical methods to epidemiology for over ten years. They have conducted several epidemiological research studies and published their work in Biometrics and the American Journal of Epidemiology, among others. Their short course on the subject of this book is in high demand in the U.S. and internationally. They are also the authors of Applied Regression Analysis (Duxbury Press, 1977).

Hal Morgenstern is Assistant Professor of Epidemiology at the Yale University School of Public Health. Dr. Morgenstern's work focuses on epidemiologic research methods and he has published extensively on the subject in the American Journal of Epidemiology, the International Journal of Epidemiology, Biometrics and the Journal of Community Health. Much of his research is applied to planning and policy issues, as well as to cardiovascular disease and to senile dementia. Dr. Morgenstern holds a Ph.D. in Epidemiology from the University of North Carolina, as well as degrees in Architecture from M.I.T. and in Regional Planning from the University of North Carolina.