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Applied Survival Analysis Using R

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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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Vörumerki: Springer Nature
Vörunúmer: 9783319312453
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Applied Survival Analysis Using R

Vörumerki: Springer Nature
Vörunúmer: 9783319312453
Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup

Veldu vöru

7.990 kr.
Fá vöru senda með tölvupósti
7.990 kr.