A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.

About the Author

DAVID W. HOSMER, Jr., PhD, is a professor of biostatistics in the Department of Biostatistics and Epidemiology of the University of Massachusetts School of Public Health and Health Sciences in Amherst, Massachusetss.

STANLEY LEMESHOW, PhD, is a professor of biostatistics in the Department of Statistics at The Ohio State University.

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