E-raamat: Statistical Methods for Survival Data Analysis

Lee (biostatistics and epidemiology) and Wang (American Indian health, both U. of Oklahoma-Oklahoma City) present a textbook for students, applied statisticians, epidemiologists, medical researchers, and others interested in analyzing survival data. They include in survival data not only the time to a certain event, but also the occurrence--yes or no--of an event, because in many cases the exact time is unknown, and the only information available is whether the event has occurred. The topics include examples of survival data analysis, some well-known parametric survival distributions and their applications, parametric methods for comparing two survival distributions, non-proportional hazards models for identifying prognostic factors related to survival time, and identifying risk factors related to dichotomous and polychotomous outcomes. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)

Praise for the Third Edition

“. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research

Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences.

Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes:

• Marginal and random effect models for analyzing correlated censored or uncensored data
• Multiple types of two-sample and K-sample comparison analysis
• Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models
• Expanded coverage of the Cox proportional hazards model
• Exercises at the end of each chapter to deepen knowledge of the presented material

Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.