Competing Risks
A Practical Perspective
Statistics in Practice
1. Edition August 2006
242 Pages, Hardcover
Wiley & Sons Ltd
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
Competing Risks
A Practical Perspective.
The term 'competing risks' refers to the situation when more than one type of failure can occur, and the observation of one type of failure hinders the observation of another. The need to understand, interpret and analyse competing risk data is key to the development of numerous areas of science. There are many research examples in which a specific type of failure is of interest, but practical issues make it extremely difficult to observe the time to the event of interest. Analyzing time to failure data in the presence of competing risks requires special Statistical tools. Competing Risks adopts a practical approach, with exercises and detailed examples throughout, using real data from cancer research.
* Provides a comprehensive overview of he interpretation and analysis of competing risks.
* Covers the main stages of a statistical analysis: planning and sample size calculation, analysis and interpretation.
* Compares and contrasts both methods for analysing competing risks: cause specific hazard and hazard of subdistribution.
* Presents the software available to perform the analysis in R, and includes macros for analysis in SAS.
* Supplemented by a website featuring data sets,software and further material.
* Competing Risks provides a practical guide to the area. The book is ideal for statisticians working in medical research, the pharmaceutical industry or public health. It will also prove invaluable for graduate students in applied statistics and biostatistics, as well as researchers in the medical field. The examples are chose from the medical field, however the methodology can be extended to any other research area where competing risks appear,such as sociology, economics and engineering.
STATISTICS IN PRACTICE
A series of practical books outlining the use of statistical techniques in a wide range of applications areas:
* HUMAN AND BIOLOGICAL SCIENCES
* EARTH AND ENVIRONMENTAL SCIENCES
* INDUSTRY, COMMERCE AND FINANCE
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.
B.1 The R software and cmprsk package.
B.2 Importing datasets in SAS.
B.3 Other programs written for R.
B.4 SAS macros for competing risk analysis.
References.
Index.
* Provides a comprehensive overview of the interpretation and analysis of competing risks.
* Covers the main stages of a statistical analysis: planning and sample size calculation, analysis and interpretation.
* Compares and contrasts both methods for analysing competing risks: cause specific hazard and hazard of subdistribution.
* Presents the software available to perform the analysis in R, and includes macros for analysis in SAS.
* Supplemented by a website featuring data sets, software and further material.
Competing Risks provides a practical guide to the area. The book is ideal for statisticians working in medical research, the pharmaceutical industry or public health. It will also prove invaluable for graduate students in applied statistics and biostatistics, as well as researchers in the medical field. The examples are chosen from the medical field, however the methodology can be extended to any other research area where competing risks appear, such as sociology, economics and engineering.
