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"Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increased considerably in all areas of scientific inquiry, mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research." "Analyses throughout the text are performed using Stata Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a reference for practitioners and resarchers in any health-related field or for professionals in insurance and government."--Jacket

"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--

Blacks have been shown to undergo fewer medical procedures than Whites, particularly for coronary disease. This article uses logistic regression to analyze racial disparities in procedure use among older Medicare enrollees admitted for coronary artery disease, stroke, hip fracture, and colon and breast cancer. Medicare enrollment and claims data are used to identify hospitalizations and procedure use, and to measure other individual-level information. No racial differences in procedure use are found for hip fracture and colon/breast cancer patients, life-threatening diseases for which basic approaches to evaluation and treatment are widely accepted. Substantial differences, however, are found for coronary disease and stroke, two illnesses whose treatment involves substantially greater discretion.