Applied Survival Analysis Regression Modeling of Time-to-Event Data

by Hosmer, David W.; Lemeshow, Stanley; May, Susanne

• ISBN13: 9780471754992
• ISBN10: 0471754994
• Edition: 2nd
• Format: Hardcover
• Copyright: 2008-03-07
• Publisher: Wiley-Interscience

Summary

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

Preface

Introduction to Regression Modeling of Survival Data

Introduction

Typical Censoring Mechanisms

Example Data Sets

Exercises

Descriptive Methods for Survival Data

Introduction

Estimating the Survival function

Using the Estimated Survival function

Comparison of Survival Functions

Other Functions of Survival Time and Their Estimators

Exercises

Regression Models for Survival Data

Introduction

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

Exercises

Interpretation of a Fitted Proportional Hazards Regression Model

Introduction

Nominal Scale Covariate

Continuous Scale Covariate

Multiple-Covariate Models

Interpreting and Using the Estimated Covariate-Adjusted Survival function

Exercises

Model Development

Introduction

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

Exercises

Assessment of Model Adequacy

Introduction

Residuals

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

Exercises

Extensions of the Proportional Hazards Model

Introduction

The Stratified Proportional Hazards Model

Time-Varying Covariates

Truncated, Left Censored and Interval Censored Data

Exercises

Parametric Regression Models

Introduction

The Exponential Regression Model

The Weibull Regression Model

The Log-Logistic Regression Model

Other Parametric Regression Models

Exercises

Other Models and Topics

Introduction

Recurrent Event Models

Frailty Models

Nested Case-Control Studies

Additive Models

Competing Risk Models

Sample Size and Power

Missing Data

Exercises

The Delta Method

An Introduction to the Counting Process Approach to Survival Analysis

Percentiles for Computation of the Hall and Wellner Confidence Band

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