Competing Risks: A Practical Perspective

×

Competing Risks: A Practical Perspective

Close

The need to understand, interpret and analyse 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, and incorporating modern techniques. This book 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 Inside Flap

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 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.