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El. knyga: Classical Competing Risks [Taylor & Francis e-book]

(Imperial College, University of London, UK)
  • Formatas: 200 pages, 500 equations; 11 Tables, black and white
  • Išleidimo metai: 11-May-2001
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-13: 9780429119217
Kitos knygos pagal šią temą:
  • Taylor & Francis e-book
  • Kaina: 60,00 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standartinė kaina: 85,72 €
  • Sutaupote 30%
  • Formatas: 200 pages, 500 equations; 11 Tables, black and white
  • Išleidimo metai: 11-May-2001
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-13: 9780429119217
Kitos knygos pagal šią temą:
If something can fail, it can often fail in one of several ways and sometimes in more than one way at a time. There is always some cause of failure, and almost always, more than one possible cause. In one sense, then, survival analysis is a lost cause. The methods of Competing Risks have often been neglected in the survival analysis literature.

Written by a leading statistician, Classical Competing Risks thoroughly examines the probability framework and statistical analysis of data of Competing Risks. The author explores both the theory of the subject and the practicalities of fitting the models to data. In a coherent, self-contained, and sequential account, the treatment moves from the bare bones of the Competing Risks setup and the associated likelihood functions through survival analysis using hazard functions. It examines discrete failure times and the difficulties of identifiability, and concludes with an introduction to the counting-process approach and the associated martingale theory.

With a dearth of modern treatments on the subject and the importance of its methods, this book fills a long-standing gap in the literature with a carefully organized exposition, real data sets, numerous examples, and clear, readable prose. If you work with lifetime data, Classical Competing Risks presents a modern, comprehensive overview of the methodology and theory you need.
Continuous failure times and their causes
1(18)
Basic probability functions
1(5)
Univariate survival distributions
1(3)
A time and a cause: Competing Risks
4(2)
Some small data sets
6(4)
Hazard functions
10(3)
Sub-hazards and overall hazard
10(2)
Proportional hazards
12(1)
Regression models
13(6)
Proportional hazards
15(1)
Accelerated life
16(1)
Proportional odds
16(1)
Mean residual life
16(3)
Parametric likelihood inference
19(18)
The likelihood for Competing Risks
19(3)
Forms of the likelihood function
19(1)
Incomplete observation of C or T
20(1)
Maximum likelihood estimates
21(1)
Model checking
22(2)
Goodness of fit
22(1)
Residuals
23(1)
Inference
24(3)
Hypothesis tests and confidence intervals
24(1)
Bayesian approach
24(2)
Bayesian computation
26(1)
Some examples
27(7)
Masked systems
34(3)
Latent failure times: probability distributions
37(20)
Basic probability functions
37(3)
Multivariate survival distributions
37(1)
Latent lifetimes
38(2)
Some examples
40(6)
Marginal vs. sub-distributions
46(2)
Independent risks
48(5)
The Makeham assumption
50(1)
Proportional hazards
51(1)
Some examples
52(1)
A risk-removal model
53(4)
Likelihood functions for univariate survival data
57(26)
Discrete and continuous failure times
57(5)
Discrete survival distributions
58(1)
Mixed survival distributions
59(3)
Discrete failure times: estimation
62(5)
Random samples: Parametric estimation
62(1)
Random samples: non-parametric estimation
63(2)
Explanatory variables
65(1)
Interval-censored data
66(1)
Continuous failure times: random samples
67(2)
The Kaplan-Meier estimator
67(1)
The integrated and cumulative hazard functions
68(1)
Continuous failure times: explanatory variables
69(7)
Cox's proportional hazards model
69(1)
Cox's partial likelihood
70(2)
The baseline survivor function
72(1)
Residuals
73(1)
Some theory for partial likelihood
74(2)
Discrete failure times again
76(2)
Proportional hazards
76(1)
Proportional odds
77(1)
Time-dependent covariates
78(5)
Discrete failure times in Competing Risks
83(18)
Basic probability functions
83(2)
Latent failure times
85(4)
Some examples based on Bernoulli trials
89(3)
Likelihood functions
92(9)
Parametric likelihood
93(1)
Non-parametric estimation from random samples
94(3)
Asymptotic distribution of non-parametric estimators
97(4)
Hazard-based methods for continuous failure times
101(18)
Latent failure times vs. hazard modelling
101(1)
Some examples of hazard modelling
102(4)
Non-parametric methods for random samples
106(7)
The Kaplan-Meier estimator
106(3)
Interval-censored data
109(2)
Actuarial approach
111(2)
Proportional hazards and partial likelihood
113(6)
The proportional hazards model
113(1)
The partial likelihood
114(2)
The baseline survivor functions
116(1)
Discrete failure times
117(2)
Latent failure times: identifiability crises
119(24)
The Cox-Tsiatis impasse
119(3)
More general identifiability results
122(8)
Specified marginals
130(4)
Discrete failure times
134(3)
Regression case
137(2)
Censoring of survival data
139(2)
Parametric identifiability
141(2)
Martingale counting processes in survival data
143(24)
Introduction
143(1)
Back to basics: probability spaces and conditional expectation
144(3)
Filtrations
147(1)
Martingales
148(3)
Discrete time
148(2)
Continuous time
150(1)
Counting processes
151(2)
Product integrals
153(1)
Survival data
154(5)
A single lifetime
154(1)
Independent lifetimes
155(2)
Competing risks
157(1)
Right-censoring
158(1)
Non-parametric estimation
159(2)
Survival times
159(1)
Competing risks
160(1)
Non-parametric testing
161(2)
Regression models
163(2)
Intensity models and time-dependent covariates
163(1)
Proportional hazards model
164(1)
Martingale residuals
164(1)
Epilogue
165(2)
Appendix 1 Numerical maximisation of likelihood functions 167(4)
Appendix 2 Bayesian computation 171(2)
Bibliography 173(10)
Index 183