Applied Survival Analysis : Regression Modeling of Time to Event Data

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2008-03-07
  • Publisher: Wiley-Interscience

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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. The 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.

Author Biography

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.

Table of Contents

Introduction to Regression Modeling of Survival Data
Typical Censoring Mechanisms
Example Data Sets
Descriptive Methods for Survival Data
Estimating the Survival function
Using the Estimated Survival function
Comparison of Survival Functions
Other Functions of Survival Time and Their Estimators
Regression Models for Survival Data
Semi-Parametric Regression Models
Fitting the Proportional Hazards Regression Model
Fitting the Proportional Hazards Model with Tied Survival Times
Estimating the Survival Function of the Proportional Hazards Regression Model
Interpretation of a Fitted Proportional Hazards Regression Model
Nominal Scale Covariate
Continuous Scale Covariate
Multiple-Covariate Models
Interpreting and Using the Estimated Covariate-Adjusted Survival function
Model Development
Purposeful Selection of Covariates
Methods to examine the scale of continuous covariates in the log hazard
An example of purposeful selection of covariates
Stepwise, Best-Subsets and Multivariable Fractional Polynomial Methods of Selecting Covariates
Stepwise selection of covariates
Best subsets selection of covariates
Selecting covariates and checking their scale using multivariable fractional polynomials
Numerical Problems
Assessment of Model Adequacy
Assessing the Proportional Hazards Assumption
Identification of Influential and Poorly Fit Subjects
Assessing Overall Goodness-of-Fit
Interpreting and Presenting Results From the Final Model
Extensions of the Proportional Hazards Model
The Stratified Proportional Hazards Model
Time-Varying Covariates
Truncated, Left Censored and Interval Censored Data
Parametric Regression Models
The Exponential Regression Model
The Weibull Regression Model
The Log-Logistic Regression Model
Other Parametric Regression Models
Other Models and Topics
Recurrent Event Models
Frailty Models
Nested Case-Control Studies
Additive Models
Competing Risk Models
Sample Size and Power
Missing Data
The Delta Method
An Introduction to the Counting Process Approach to Survival Analysis
Percentiles for Computation of the Hall and Wellner Confidence Band
Table of Contents provided by Publisher. All Rights Reserved.

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