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El. knyga: Mathematical Methods in Survival Analysis, Reliability and Quality of Life [Wiley Online]

Edited by (Université Victor Segalen, Bordeaux 2, France), Edited by (Université de Paris René Descartes, France), Edited by (University of Technology of Compičgne, France), Edited by (Université Pierre et Marie Curie, Paris 6, France)
  • Formatas: 420 pages
  • Serija: ISTE
  • Išleidimo metai: 10-Jun-2008
  • Leidėjas: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 470610980
  • ISBN-13: 9780470610985
Kitos knygos pagal šią temą:
  • Wiley Online
  • Kaina: 249,52 €*
  • * this price gives unlimited concurrent access for unlimited time
Mathematical Methods in Survival Analysis, Reliability and Quality of LifeDidesnis paveikslėlis
  • Formatas: 420 pages
  • Serija: ISTE
  • Išleidimo metai: 10-Jun-2008
  • Leidėjas: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 470610980
  • ISBN-13: 9780470610985
Kitos knygos pagal šią temą:
Wiley Online E-booksMore info about Wiley Online
Partnerių interneto svetainė: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470610985
Based primarily on presentations at the eponymous seminar held in Europe in 2006, 21 papers describe work that considers key issues without excluding either the technical or the human factors. Topics include model selection for additive regression, estimation of conditional probabilities under bias sampling, inference in transformation models for arbitrarily truncated and censored data, applications of Poisson models and semi-Markov processes, bivariate Cox models, estimation of a class of survival functions, approximate likelihood, Cox regression with missing values of a covariate having an effect on risk of failure, Bayesian variable sampling, fatigue crack growth modeling, redundant systems with one standby unit, life testing when the hazard rate function is cup-shaped, point processes in software reliability, the aspects of Markov and Rasch models, internal analysis and validation of a quality of life instrument for French diabetics, deterministic modeling of HIV/AIDS, and models useful in sport sciences. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.
Preface 13(2)
PART I
15(116)
Model Selection for Additive Regression in the Presence of Right-Censoring
17(16)
Elodie Brunel
Fabienne Comte
Introduction
17(1)
Assumptions on the model and the collection of approximation spaces
18(2)
Non-parametric regression model with censored data
18(1)
Description of the approximation spaces in the univariate case
19(1)
The particular multivariate setting of additive models
20(1)
The estimation method
20(2)
Transformation of the data
20(1)
The mean-square contrast
21(1)
Main result for the adaptive mean-square estimator
22(1)
Practical implementation
23(7)
The algorithm
23(1)
Univariate examples
24(3)
Bivariate examples
27(1)
A trivariate example
28(2)
Bibliography
30(3)
Non-parametric Estimation of Conditional Probabilities, Means and Quantiles under Bias Sampling
33(16)
Odile Pons
Introduction
33(1)
Non-parametric estimation of p
34(1)
Bias depending on the value of Y
35(2)
Bias due to truncation on X
37(1)
Truncation of a response variable in a non-parametric regression model
37(5)
Double censoring of a response variable in a non-parametric model
42(2)
Other truncation and censoring of Y in a non-parametric model
44(3)
Observation by interval
47(1)
Bibliography
48(1)
Inference in Transformation Models for Arbitrarily Censored and Truncated Data
49(12)
Filia Vonta
Catherine Huber
Introduction
49(1)
Non-parametric estimation of the survival function S
50(1)
Semi-parametric estimation of the survival function S
51(3)
Simulations
54(5)
Bibliography
59(2)
Introduction of Within-area Risk Factor Distribution in Ecological Poisson Models
61(14)
Lea Fortunato
Chantal Guihenneuc-Jouyaux
Dominique Laurier
Margot Tirmarche
Jacqueline Clavel
Denis Hemon
Introduction
61(1)
Modeling framework
62(3)
Aggregated model
62(3)
Prior distributions
65(1)
Simulation framework
65(1)
Results
66(5)
Strong association between relative risk and risk factor, correlated within-area means and variances (mean-dependent case)
67(1)
Sensitivity to within-area distribution of the risk factor
68(1)
Application: leukemia and indoor radon exposure
69(2)
Discussion
71(1)
Bibliography
72(3)
Semi-Markov Processes and Usefulness in Medicine
75(18)
Eve Mathieu-Dupas
Claudine Gras-Aygon
Jean-Pierre Daures
Introduction
75(1)
Methods
76(6)
Model description and notation
76(3)
Construction of health indicators
79(3)
An application to HIV control
82(4)
Context
82(1)
Estimation method
82(2)
Results: new indicators of health state
84(2)
An application to breast cancer
86(3)
Context
86(1)
Age and stage-specific prevalence
87(1)
Estimation method
88(1)
Results: indicators of public health
88(1)
Discussion
89(1)
Bibliography
89(4)
Bivariate Cox Models
93(16)
Michel Broniatowski
Alexandre Depire
Ya'acov Ritov
Introduction
93(1)
A dependence model for duration data
93(2)
Some useful facts in bivariate dependence
95(3)
Coherence
98(4)
Covariates and estimation
102(2)
Application: regression of Spearman's rhoon covariates
104(2)
Bibliography
106(3)
Non-parametric Estimation of a Class of Survival Functional
109(12)
Belkacem Abdous
Introduction
109(2)
Weighted local polynomial estimates
111(3)
Consistency of local polynomial fitting estimators
114(2)
Automatic selection of the smoothing parameter
116(3)
Bibliography
119(2)
Approximate Likelihood in Survival Models
121(10)
Henning Lauter
Introduction
121(1)
Likelihood in proportional hazard models
122(1)
Likelihood in parametric models
122(1)
Profile likelihood
123(4)
Smoothness classes
124(1)
Approximate likelihood function
125(2)
Statistical arguments
127(2)
Bibliography
129(2)
PART II
131(106)
Cox Regression with Missing Values of a Covariate having a Non-proportional Effect on Risk of Failure
133(18)
Jean-Francois Dupuy
Eve Leconte
Introduction
133(3)
Estimation in the Cox model with missing covariate values: a short review
136(3)
Estimation procedure in the stratified Cox model with missing stratum indicator values
139(2)
Asymptotic theory
141(4)
A simulation study
145(2)
Discussion
147(2)
Bibliography
149(2)
Exact Bayesian Variable Sampling Plans for Exponential Distribution under Type-I Censoring
151(12)
Chien-Tai Lin
Yen-Lung Huang
N. Balakrishnan
Introduction
151(1)
Proposed sampling plan and Bayes risk
152(4)
Numerical examples and comparison
156(5)
Bibliography
161(2)
Reliability of Stochastic Dynamical Systems Applied to Fatigue Crack Growth Modeling
163(16)
Julien Chiquet
Nikolaos Limnios
Introduction
163(2)
Stochastic dynamical systems with jump Markov process
165(3)
Estimation
168(2)
Numerical application
170(5)
Conclusion
175(1)
Bibliography
175(4)
Statistical Analysis of a Redundant System with One Standby Unit
179(10)
Vilijandas Bagdonavicius
Inga Masiulaityte
Mikhail Nikulin
Introduction
179(1)
The models
180(1)
The tests
181(1)
Limit distribution of the test statistics
182(5)
Bibliography
187(2)
A Modified Chi-squared Goodness-of-fit Test for the Three-parameter Weibull Distribution and its Applications in Reliability
189(14)
Vassilly Voinov
Roza Alloyarova
Natalie Pya
Introduction
189(2)
Parameter estimation and modified chi-squared tests
191(3)
Power estimation
194(1)
Neyman-Pearson classes
194(3)
Discussion
197(1)
Conclusion
198(1)
Appendix
198(3)
Bibliography
201(2)
Accelerated Life Testing when the Hazard Rate Function has Cup Shape
203(14)
Vilijandas Bagdonavicius
Luc Clerjaud
Mikhail Nikulin
Introduction
203(1)
Estimation in the AFT-GW model
204(3)
AFT model
204(1)
AFT-Weibull, AFT-lognormal and AFT-GW models
205(1)
Plans of ALT experiments
205(1)
Parameter estimation: AFT-GW model
206(1)
Properties of estimators: simulation results for the AFT-GW model
207(4)
Some remarks on the second plan of experiments
211(2)
Conclusion
213(1)
Appendix
213(2)
Bibliography
215(2)
Point Processes in Software Reliability
217(20)
James Ledoux
Introduction
217(2)
Basic concepts for repairable systems
219(2)
Self-exciting point processes and black-box models
221(4)
White-box models and Markovian arrival processes
225(9)
A Markovian arrival model
226(2)
Parameter estimation
228(4)
Reliability growth
232(2)
Bibliography
234(3)
PART III
237(78)
Likelihood Inference for the Latent Markov Rasch Model
239(16)
Francesco Bartolucci
Fulvia Pennoni
Monia Lupparelli
Introduction
239(1)
Latent class Rasch model
240(1)
Latent Markov Rasch model
241(5)
Likelihood inference for the latent Markov Rasch model
243(1)
Log-likelihood maximization
244(1)
Likelihood ratio testing of hypotheses on the parameters
245(1)
An application
246(1)
Possible extensions
247(4)
Discrete response variables
248(1)
Multivariate longitudinal data
248(3)
Conclusions
251(1)
Bibliography
252(3)
Selection of Items Fitting a Rasch Model
255(20)
Jean-Benoit Hardouin
Mounir Mesbah
Introduction
255(1)
Notations and assumptions
256(1)
Notations
256(1)
Fundamental assumptions of the Item Response Theory (IRT)
256(1)
The Rasch model and the multidimensional marginally sufficient Rasch model
256(2)
The Rasch model
256(1)
The multidimensional marginally sufficient Rasch model
257(1)
The Raschfit procedure
258(1)
A fast version of Raschfit
259(2)
Estimation of the parameters under the fixed effects Rasch model
259(1)
Principle of Raschfit-fast
260(1)
A model where the new item is explained by the same latent trait as the kernel
260(1)
A model where the new item is not explained by the same latent trait as the kernel
260(1)
Selection of the new item in the scale
261(1)
A small set of simulations to compare Raschfit and Raschfit-fast
261(8)
Parameters of the simulation study
261(3)
Results and computing time
264(5)
A large set of simulations to compare Raschfit-fast, MSP and HCA/CCPROX
269(1)
Parameters of the simulations
269(1)
Discussion
270(1)
The Stata module ``Raschfit''
270(1)
Conclusion
271(2)
Bibliography
273(2)
Analysis of Longitudinal HrQoL using Latent Regression in the Context of Rasch Modeling
275(16)
Silvia Bacci
Introduction
275(1)
Global models for longitudinal data analysis
276(2)
A latent regression Rasch model for longitudinal data analysis
278(5)
Model structure
278(2)
Correlation structure
280(1)
Estimation
281(1)
Implementation with SAS
281(2)
Case study: longitudinal HrQoL of terminal cancer patients
283(4)
Concluding remarks
287(2)
Bibliography
289(2)
Empirical Internal Validation and Analysis of a Quality of Life Instrument in French Diabetic Patients during an Educational Intervention
291(24)
Judith Chwalow
Keith Meadows
Mounir Mesbah
Vincent Coliche
Etienne Mollet
Introduction
291(1)
Material and methods
292(3)
Health care providers and patients
292(1)
Psychometric validation of the DHP
293(1)
Psychometric methods
293(1)
Comparative analysis of quality of life by treatment group
294(1)
Results
295(9)
Internal validation of the DHP
295(8)
Comparative analysis of quality of life by treatment group
303(1)
Discussion
304(1)
Conclusion
305(1)
Bibliography
306(3)
Appendices
309(6)
PART IV
315(38)
Deterministic Modeling of the Size of the HIV/AIDS Epidemic in Cuba
317(16)
Rachid Lounes
Hector de Arazoza
Y.H. Hsieh
Jose Joanes
Introduction
317(2)
The models
319(5)
The k2X model
322(1)
The k2Y model
322(1)
The k2XY model
323(1)
The k2 model
324(1)
The underreporting rate
324(1)
Fitting the models to Cuban data
325(1)
Discussion and concluding remarks
326(4)
Bibliography
330(3)
Some Probabilistic Models Useful in Sport Sciences
333(20)
Leo Gerville-Reache
Mikhail Nikulin
Sebastien Orazio
Nicolas Paris
Virginie Rosa
Introduction
333(1)
Sport jury analysis: the Gauss-Markov approach
334(3)
Gauss-Markov model
334(1)
Test for non-objectivity of a variable
334(1)
Test of difference between skaters
335(1)
Test for the less precise judge
336(1)
Sport performance analysis: the fatigue and fitness approach
337(2)
Model characteristics
337(1)
Monte Carlo simulation
338(1)
Results
339(1)
Sport equipment analysis: the fuzzy subset approach
339(4)
Statistical model used
340(1)
Sensorial analysis step
341(1)
Results
342(1)
Sport duel issue analysis: the logistic simulation approach
343(4)
Modeling by logistic regression
344(1)
Numerical simulations
345(1)
Results
345(2)
Sport epidemiology analysis: the accelerated degradation approach
347(3)
Principle of degradation in reliability analysis
347(1)
Accelerated degradation model
348(2)
Conclusion
350(1)
Bibliography
350(3)
Appendices
353(14)
A. European Seminar: Some Figures
353(4)
A.1. Former international speakers invited to the European Seminar
353(1)
A.2. Former meetings supported by the European Seminar
353(1)
A.3. Books edited by the organizers of the European Seminar
354(1)
A.4. Institutions supporting the European Seminar (names of colleagues)
355(2)
B. Contributors
357(10)
Index 367
Catherine Huber is an Emeritus professor at Université de Paris René Descartes.  Her research activity concerns nonparametric and semi-parametric theory of statistics and their applications in biology and medicine. She has several publications in particular in the field of survival analysis. She is the co-author and co-editor of several books in the above fields. Nikolaos Limnios is a professor at the University of Technology of Compičgne. His research and teaching activities concern stochastic processes, statistical inference and their applications in particular in reliability and survival analysis. He is the co-author and co-editor of several books in the above fields.

Mounir Mesbah is a professor at the Université Pierre et Marie Curie, Paris 6. His research and teaching activities concern statistics and its applications in health science and medicine (biostatistics). He is the co-author of several articles and co-editor of several books in the above fields.

Mikhail Nikulin is a professor at the Université Victor Segalen, and a member of the Institute of Mathematics at Bordeaux. His research and teaching activities concern mathematical statistics and its applications in reliability and survival analysis. He is the co-author and co-editor of several books in the above fields.
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