Author: David W. Hosmer ISBN : Product Detai New from Format: PDFSince publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase 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 to9 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.
This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.
Features of the Second Edition include:
- Expanded coverage of interactions and the covariate-adjusted survival functions
- The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
- New discussion of variable selection with multivariable fractional polynomials
- Further exploration of time-varying covariates, complex with examples
- Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
- Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
- New examples and exercises at the end of each chapter
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 valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.Direct download links available for PRETITLE Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics) [Kindle Edition] POSTTITLE
- File Size: 9768 KB
- Print Length: 416 pages
- Publisher: Wiley-Interscience; 2 edition (September 23, 2011)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B005PY79VY
- Text-to-Speech: Enabled
- Lending: Enabled
- Amazon Best Sellers Rank: #474,779 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #63 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Biological Sciences > Biostatistics
- #63 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Biological Sciences > Biostatistics
Applied Survival Analysis: Regression Modeling of Time to Event Data PDF
The authors provide a really nice, non-technical survey of the landscape for Cox Proportional Hazards models. A nice aspect of their treatment is the care they take to reference all highly technical texts and journal articles. For example, if you'd like to find out more about goodness-of-fit tests for survival models, the authors provide ample references to the Counting Process Theory of Martingale Residuals.
The first chapter discusses the basic characteristics of survival data, including the notion of censoring (in all of its various forms). Examples of the principle types of censoring are included. The chapter also includes introductory material on the general survival model, including a nice description of the log likelihood function. Curiously, the rigorous definition of the hazard function has been omitted, probably to avoid intimidating readers who are not familiar with formal limits.
Chapter 2 continues to build up the general survival model and introduces the relationship between the survivor function and the cumulative hazard. Pointwise estimators for the survivor function are discussed, including the Kaplan-Meier estimator along with the various variance estimators. Test statistics for comparing two survival populations are introduced, including the Log-Rank and General Wilcoxon statistics. The reader is encouraged to read the counting process treatments of these statistics to see why they produced defensible hypothesis tests.
Chapter 3 is devoted to the Cox Model and Cox's partial likelihood function. Tests for significance of the coefficients are introduced, included the Wald test, log likelihood ratio test and the score test. These are used heavily in the later chapters as the basis of a model-building methodology.
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