Short description The need to understand, interpret, and analyze competing risk data is key to many areas of science, particularly medical research. There is a real need for a book that presents an overview of methodology used in the interpretation and analysis of competing risks, with a focus on practical applications to medical problems, while incorporating modern techniques. Competing Risks: A Practical Perspective fills that need by presenting the most up-to-date methodology in a way that can be readily understood, and applied, by the practitioner
From the contents Preface.
Acknowledgements.
1. Introduction.
1.1 Historical notes.
1.2 Defining competing risks.
1.3 Use of the Kaplan-Meier method in the presence of competing risks.
1.4 Testing in the competing risk framework.
1.5 Sample size calculation.
1.6 Examples.
2. Survival - basic concepts.
2.1 Introduction.
2.2 Definitions and background formulae.
2.3 Estimation and hypothesis testing.
2.4 Software for survival analysis.
2.5 Closing remarks.
3. Competing risks - definitions.
3.1 Recognizing competing risks.
3.2 Two mathematical definitions.
3.3 Fundamental concepts.
3.4 Closing remarks.
4. Descriptive methods for competing risks data.
4.1 Product-limit estimator and competing risks.
4.2 Cumulative incidence function.
4.3 Software and examples.
4.4 Closing remarks.
5. Testing a covariate.
5.1 Introduction.
5.2 Testing a covariate.
5.3 Software and examples.
5.4 Closing remarks.
6. Modelling in the presence of competing risks.
6.1 Introduction.
6.2 Modelling the hazard of the cumulative incidence function.
6.3 Cox model and competing risks.
6.4 Checking the model assumptions.
6.5 Closing remarks.
7. Calculating the power in the presence of competing risks.
7.1 Introduction.
7.2 Sample size calculation when competing risks are not present.
7.3 Calculating power in the presence of competing risks.
7.4 Examples.
7.5 Closing remarks.
8. Other issues in competing risks.
8.1 Conditional probability function.
8.2 Comparing two types of risk in the same population.
8.3 Identifiability and testing independence.
8.4 Parametric modelling.
9. Food for thought.
Problem 1: Estimation of the probability of the event of interest.
Problem 2: Testing a covariate.
Problem 3: Comparing the event of interest between two groups when the competing risks are different for each group.
Problem 4: Information needed for sample size calculations.
Problem 5: The effect of the size of the incidence of competing risks on the coefficient obtained in the model.
Problem 6: The KLY test and the non-proportionality of hazards.
Problem 7: The KLY and Wilcoxon tests.
A: Theoretical background.
A.1 Nonparametric maximum likelihood estimation for the survivor function in the discrete case.
A.2 Confidence interval for survivor function.
A.3 The Variance for Gray's test.
A.4 Derivation of the parameters for the exponential latent failure time model.
A.5 Likelihood of a mixture of exponentials in the bivariate approach.
B: Analysing competing risks data using R and SAS.
