Efnisyfirlit

• Preface
• Contents
• 1 Introduction
  • 1.1 What Is Survival Analysis?
  • 1.2 What You Need to Know to Use This Book
  • 1.3 Survival Data and Censoring
  • 1.4 Some Examples of Survival Data Sets
  • 1.5 Additional Notes
  • Exercises
• 2 Basic Principles of Survival Analysis
  • 2.1 The Hazard and Survival Functions
  • 2.2 Other Representations of a Survival Distribution
  • 2.3 Mean and Median Survival Time
  • 2.4 Parametric Survival Distributions
  • 2.5 Computing the Survival Function from the Hazard Function
  • 2.6 A Brief Introduction to Maximum Likelihood Estimation
  • 2.7 Additional Notes
  • Exercises
• 3 Nonparametric Survival Curve Estimation
  • 3.1 Nonparametric Estimation of the Survival Function
  • 3.2 Finding the Median Survival and a Confidence Interval for the Median
  • 3.3 Median Follow-Up Time
  • 3.4 Obtaining a Smoothed Hazard and Survival Function Estimate
  • 3.5 Left Truncation
  • 3.6 Additional Notes
  • Exercises
• 4 Nonparametric Comparison of Survival Distributions
  • 4.1 Comparing Two Groups of Survival Times
  • 4.2 Stratified Tests
  • 4.3 Additional Note
  • Exercises
• 5 Regression Analysis Using the Proportional Hazards Model
  • 5.1 Covariates and Nonparametric Survival Models
  • 5.2 Comparing Two Survival Distributions Using a Partial Likelihood Function
  • 5.3 Partial Likelihood Hypothesis Tests
    • 5.3.1 The Wald Test
    • 5.3.2 The Score Test
    • 5.3.3 The Likelihood Ratio Test
  • 5.4 The Partial Likelihood with Multiple Covariates
  • 5.5 Estimating the Baseline Survival Function
  • 5.6 Handling of Tied Survival Times
  • 5.7 Left Truncation
  • 5.8 Additional Notes
  • Exercises
• 6 Model Selection and Interpretation
  • 6.1 Covariate Adjustment
  • 6.2 Categorical and Continuous Covariates
  • 6.3 Hypothesis Testing for Nested Models
  • 6.4 The Akaike Information Criterion for Comparing Non-nested Models
  • 6.5 Including Smooth Estimates of Continuous Covariates in a Survival Model
  • 6.6 Additional Note
  • Exercises
• 7 Model Diagnostics
  • 7.1 Assessing Goodness of Fit Using Residuals
    • 7.1.1 Martingale and Deviance Residuals
    • 7.1.2 Case Deletion Residuals
  • 7.2 Checking the Proportion Hazards Assumption
    • 7.2.1 Log Cumulative Hazard Plots
    • 7.2.2 Schoenfeld Residuals
  • 7.3 Additional Note
  • Exercises
• 8 Time Dependent Covariates
  • 8.1 Introduction
  • 8.2 Predictable Time Dependent Variables
    • 8.2.1 Using the Time Transfer Function
    • 8.2.2 Time Dependent Variables That Increase Linearly with Time
  • 8.3 Additional Note
  • Exercises
• 9 Multiple Survival Outcomes and Competing Risks
  • 9.1 Clustered Survival Times and Frailty Models
    • 9.1.1 Marginal Survival Models
    • 9.1.2 Frailty Survival Models
    • 9.1.3 Accounting for Family-Based Clusters in the ``ashkenazi'' Data
    • 9.1.4 Accounting for Within-Person Pairing of Eye Observations in the Diabetes Data
  • 9.2 Cause-Specific Hazards
    • 9.2.1 Kaplan-Meier Estimation with Competing Risks
    • 9.2.2 Cause-Specific Hazards and Cumulative Incidence Functions
    • 9.2.3 Cumulative Incidence Functions for ProstateCancer Data
    • 9.2.4 Regression Methods for Cause-Specific Hazards
    • 9.2.5 Comparing the Effects of Covariates on Different Causes of Death
  • 9.3 Additional Notes
  • Exercises
• 10 Parametric Models
  • 10.1 Introduction
  • 10.2 The Exponential Distribution
  • 10.3 The Weibull Model
    • 10.3.1 Assessing the Weibull Distribution as a Model for Survival Data in a Single Sample
    • 10.3.2 Maximum Likelihood Estimation of Weibull Parameters for a Single Group of Survival Data
    • 10.3.3 Profile Weibull Likelihood
    • 10.3.4 Selecting a Weibull Distribution to Model Survival Data
    • 10.3.5 Comparing Two Weibull Distributions Using the Accelerated Failure Time and Proportional Hazar
    • 10.3.6 A Regression Approach to the Weibull Model
    • 10.3.7 Using the Weibull Distribution to Model Survival Data with Multiple Covariates
    • 10.3.8 Model Selection and Residual Analysis with Weibull Survival Data
  • 10.4 Other Parametric Survival Distributions
  • 10.5 Additional Note
  • Exercises
• 11 Sample Size Determination for Survival Studies
  • 11.1 Power and Sample Size for a Single Arm Study
  • 11.2 Determining the Probability of Death in a Clinical Trial
  • 11.3 Sample Size for Comparing Two Exponential Survival Distributions
  • 11.4 Sample Size for Comparing Two Survival Distributions Using the Log-Rank Test
  • 11.5 Determining the Probability of Death from a Non-parametric Survival Curve Estimate
  • 11.6 Example: Calculating the Required Number of Patients for a Randomized Study of Advanced Gastric
  • 11.7 Example: Calculating the Required Number of Patients for a Randomized Study of Patients with Me
  • 11.8 Using Simulations to Estimate Power
  • 11.9 Additional Notes
  • Exercises
• 12 Additional Topics
  • 12.1 Using Piecewise Constant Hazards to Model Survival Data
  • 12.2 Interval Censoring
  • 12.3 The Lasso Method for Selecting Predictive Biomarkers
  • Exercises
• Erratum to
• A A Basic Guide to Using R for Survival Analysis
  • A.1 The R System
    • A.1.1 A First R Session
    • A.1.2 Scatterplots and Fitting Linear Regression Models
    • A.1.3 Accommodating Non-linear Relationships
    • A.1.4 Data Frames and the Search Path for Variable Names
    • A.1.5 Defining Variables Within a Data Frame
    • A.1.6 Importing and Exporting Data Frames
  • A.2 Working with Dates in R
    • A.2.1 Dates and Leap Years
    • A.2.2 Using the ``as.date'' Function
  • A.3 Presenting Coefficient Estimates Using Forest Plots
  • A.4 Extracting the Log Partial Likelihood and Coefficient Estimates from a coxph Object
  • References
• Index
• R Package Index

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