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<p>Preface xi</p> <p>1. Introduction to Regression Modeling of Survival Data 1</p> <p>2. Descriptive Methods for Survival Data 16</p> <p>3. Regression Models for Survival Data 67</p> <p>4. Interpretation of a Fitted Proportional Hazards Regression Model 92</p> <p>5. Model Development 132</p> <p>6. Assessment of Model Adequacy 169</p> <p>7. Extensions of the Proportional Hazards Model 207</p> <p>8. Parametric Regression Models 244</p> <p>9. Other Models and Topics 286</p> <p>Appendix 1: The Delta Method 355</p> <p>Appendix 2: An Introduction to the Counting Process Approach to Survival Analysis 359</p> <p>Appendix 3: Percentiles for Computation of the Hall and Wellner Confidence Band 364</p> <p>References 365</p> <p>Index 383</p>

<p>“This is a great book for anyone analyzing time-to-event data.  Researchers interested in the underlying theory will have to go elsewhere..”  (<i>Stat Papers</i>, 1 December 2012)</p> "It is well suited for teaching a graduate-level course in medical statistics, and the data sets used in the book are available online." (<i>Biometrical Journal</i>, August 2009) <p>"This is a superb resource - a practical guide with up-to-date applications. The authors are excellent teachers of the mathematics and application of survival data regression modeling." (<i>Doodys</i>, August 2009)</p> <p>"The extensive and detailed coverage of the process of survival model fitting, as well as the applied exercises, make this textbook an excellent choice for an applied survival analysis course." (<i>Journal of Biopharmaceutical Statistics</i>, Volume 18, Issue 6, 2008)</p>

<b>David W. Hosmer, PhD,</b> is Professor Emeritus of Biostatistics in the School of Public Health and Heatlth Sciences at the University of Massachusetts Amherst. Dr. Hosmer is the coauthor of <i>Applied Logistic Regression</i>, published by Wiley. <p><b>Stanley Lemeshow, PhD,</b> is Professor and Dean of the College of Public Health at The Ohio State University. Dr. Lemeshow has over thirty-five years of academic experience in the areas of regression, categorical data methods, and sampling methods. He is the coauthor of <i>Sampling of Population: Methods and Application</i> and <i>Applied Logistic Regression</i>, both published by Wiley.</p> <p><b>Susanne May, PhD,</b> is Assistant Professor of Biostatistics at the University of California, San Diego. Dr. May has over twelve years of experience in providing statistical support for health-related research projects.</p>

<b>THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION</b> <p>Since 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. <i>Applied Survival Analysis, Second Edition</i> 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.</p> <p>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.</p> <p>Features of the <i>Second Edition</i> include:</p> <ul> <li>Expanded coverage of interactions and the covariate-adjusted survival functions</li> <li>The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques</li> <li>New discussion of variable selection with multivariable fractional polynomials</li> <li>Further exploration of time-varying covariates, complex with examples</li> <li>Additional treatment of the exponential, Weibull, and log-logistic parametric regression models</li> <li>Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values</li> <li>New examples and exercises at the end of each chapter</li> </ul> <p>Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. <i>Applied Survival Analysis, Second Edition</i> 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.</p>

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