Melania Pintilie

Competing Risks

• Gebundenes Buch

Produktbeschreibung

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

Produktdetails

• Statistics in Practice
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 240
• Erscheinungstermin: 1. Oktober 2006
• Englisch
• Abmessung: 235mm x 157mm x 18mm
• Gewicht: 465g
• ISBN-13: 9780470870686
• ISBN-10: 0470870680
• Artikelnr.: 20869978

Autorenporträt

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.

Inhaltsangabe

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. 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.

Rezensionen

"Will help statisticians and researchers understand the complexity of the competing-risks problem and to complete the analysis. I am glad to have it on my shelf." ( Technometrics , August 2008)
"...a concise introduction to the field of competing risks in survival analysis, especially useful for practitioners and researchers in the biostatistics field." ( Zentralblatt MATH , 2007)