Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition

David W. Hosmer, PhD, 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 Applied Logistic Regression , published by Wiley. Stanley Lemeshow, PhD, 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 Sampling of Population: Methods and Application and Applied Logistic Regression , both published by Wiley. Susanne May, PhD, 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.

Preface. 1. Introduction to Regression Modeling of Survival Data. 1.1 Introduction. 1.2 Typical Censoring Mechanisms. 1.3 Example Data Sets. Exercises. 2. Descriptive Methods for Survival Data. 2.1 Introduction. 2.2 Estimating the Survival Function. 2.3 Using the Estimated Survival Function. 2.4 Comparison of Survival Functions. 2.5 Other Functions of Survival Time and Their Estimators. Exercises. 3. Regression Models for Survival Data. 3.1 Introduction. 3.2 Semi-Parametric Regression Models. 3.3 Fitting the Proportional Hazards Regression Model. 3.4 Fitting the Proportional Hazards Model with Tied Survival Times. 3.5 Estimating the Survival Function of the Proportional Hazards Regression Model. Exercises. 4. Interpretation of a Fitted Proportional Hazards Regression Model. 4.1 Introduction. 4.2 Nominal Scale Covariate. 4.3 Continuous Scale Covariate. 4.4 Multiple-Covariate Models. 4.5 Interpreting and Using the Estimated Covariate-Adjusted Survival Function. Exercises. 5. Model Development. 5.1 Introduction. 5.2 Purposeful Selection of Covariates. 5.2.1 Methods to examine the scale of continuous covariates in the log hazard. 5.2.2 An example of purposeful selection of covariates. 5.3 Stepwise, Best-Subsets and Multivariable Fractional Polynomial Methods of Selecting Covariates. 5.3.1 Stepwise selection of covariates. 5.3.2 Best subsets selection of covariates. 5.3.3 Selecting covariates and checking their scale using multivariable fractional polynomials. 5.4 Numerical Problems. Exercises. 6. Assessment of Model Adequacy. 6.1 Introduction. 6.2 Residuals. 6.3 Assessing the Proportional Hazards Assumption. 6.4 Identification of Influential and Poorly Fit Subjects. 6.5 Assessing Overall Goodness-of-Fit. 6.6 Interpreting and Presenting Results From the Final Model. Exercises. 7. Extensions of the Proportional Hazards Model. 7.1 Introduction. 7.2 The Stratified Proportional Hazards Model. 7.3 Time-Varying Covariates. 7.4 Truncated, Left Censored and Interval Censored Data. Exercises. 8. Parametric Regression Models. 8.1 Introduction. 8.2 The Exponential Regression Model. 8.3 The Weibull Regression Model. 8.4 The Log-Logistic Regression Model. 8.5 Other Parametric Regression Models. Exercises. 9. Other Models and Topics. 9.1 Introduction. 9.2 Recurrent Event Models. 9.3 Frailty Models. 9.4 Nested Case-Control Studies. 9.5 Additive Models. 9.6 Competing Risk Models. 9.7 Sample Size and Power. 9.8 Missing Data. Exercises. Appendix 1: The Delta Method. Appendix 2: An Introduction to the Counting Process Approach to Survival Analysis. Appendix 3: Percentiles for Computation of the Hall and Wellner Confidence Band. References. Index.