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Informationen zum Autor ELISA T. LEE, PhD, is Regents Professor and George Lynn Cross Research Professor of Biostatistics and Epidemiology and Director of the Center for American Indian Health Research at the University of Oklahoma Health Sciences Center.JOHN Wenyu WANG, PhD, is Professor of Research at the Center for American Indian Health Research at the University of Oklahoma Health Sciences Center. Klappentext 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 ResearchUpdated 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 materialStatistical 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. Zusammenfassung Upgraded to reflect the latest research and software applications on the topic, this new edition continues to provide a comprehensive introduction to the statistical methods for analyzing survival data. Inhaltsverzeichnis Preface xi1 Introduction 11.1 Preliminaries 11.2 Censored Data 21.3 Scope of the Book 52 Functions of Survival Time 82.1 Definitions 82.2 Relationships of the Survival Functions 15Exercises 163 Examples of Survival Data Analysis 193.1 Example 3.1: Comparison of Two Treatments and Three Diets 193.2 Example 3.2: Comparison of Two Survival Patterns Using Life Tables 263.3 Example 3.3: Fitting Survival Distributions to Tumor-Free Times 283.4 Example 3.4: Comparing Survival of a Cohort with that of a General Population -- Relative Survival 303.5 Example 3.5: Identification of Risk Factors for Incident Events 333.6 Example 3.6: Identification of Risk Factors for the Prevalence of Age-Related Macular Degeneration 383.7 Example 3.7: Identification of Significant Risk Factors for Incident Hypertension Using Related Data (Repeated Measurements) in a Longitudinal Study 46Exercises 544 Nonparametric Methods of Estimating Survival Functions 684.1 Product-Limit Estimates of Survivorship Function 694.2 N elson-Aalen Estimates of Survivorship Function 824.3 Life-Table Analysis 834.4 Relative Survival Rates 964.5 Standardized Rates and Ratios 98Exercises 1045 Nonparametric Methods for Comparing Survival Distributions 1085.1 Comparison of Two Survival Distributions 1085.2 The Mantel and Haenszel Test 1235.3 Comparison of K (K > 2) Samples 128Exercises 1306 Some Well-Known Parametric Survival Distributions And Their Applications 1336.1 Exponen...
