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  1. Competing risks
    a practical perspective
    Author: Pintilie, Melania
    Published: 2006
    Publisher:  Wiley, Chichester [u.a.]

    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... more

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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Location:
    Otto-von-Guericke-Universität, Universitätsbibliothek
    Signature:
    eBook Wiley
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    Location:
    Bibliotheks-und Informationssystem der Carl von Ossietzky Universität Oldenburg (BIS)
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    Location:
    Bibliotheks-und Informationssystem der Carl von Ossietzky Universität Oldenburg (BIS)
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    Location:
    Bibliotheks-und Informationssystem der Carl von Ossietzky Universität Oldenburg (BIS)
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    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. 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

     
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9780470870709; 0470870702; 1282123866; 1280722223; 9781282123861; 9781280722226
    Other identifier:
    RVK Categories: SK 850
    Series: Statistics in practice
    Subjects: Competing risks; Statistik; Risikotheorie
    Scope: Online-Ressource (Text + Images)
    Notes:

    Includes bibliographical references (p. 209-213) and index

    Electronic reproduction; Available via World Wide Web

    Competing Risks; Contents; 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; 1.6.1 Tamoxifen trial; 1.6.2 Hypoxia study; 1.6.3 Follicular cell lymphoma study; 1.6.4 Bone marrow transplant study; 1.6.5 Hodgkin's disease study; 2 Survival - basic concepts; 2.1 Introduction; 2.2 Definitions and background formulae; 2.2.1 Introduction; 2.2.2 Basic mathematical formulae

    2.2.3 Common parametric distributions2.2.4 Censoring and assumptions; 2.3 Estimation and hypothesis testing; 2.3.1 Estimating the hazard and survivor functions; 2.3.2 Nonparametric testing: log-rank and Wilcoxon tests; 2.3.3 Proportional hazards model; 2.4 Software for survival analysis; 2.5 Closing remarks; 3 Competing risks - definitions; 3.1 Recognizing competing risks; 3.1.1 Practical approaches; 3.1.2 Common endpoints in medical research; 3.2 Two mathematical definitions; 3.2.1 Competing risks as bivariate random variable; 3.2.2 Competing risks as latent failure times

    3.3 Fundamental concepts3.3.1 Competing risks as bivariate random variable; 3.3.2 Competing risks as latent failure times; 3.3.3 Discussion of the two approaches; 3.4 Closing remarks; 4 Descriptive methods for competing risks data; 4.1 Product-limit estimator and competing risks; 4.2 Cumulative incidence function; 4.2.1 Heuristic estimation of the CIF; 4.2.2 Nonparametric maximum likelihood estimation of the CIF; 4.2.3 Calculating the CIF estimator; 4.2.4 Variance and confidence interval for the CIF estimator; 4.3 Software and examples; 4.3.1 Using R; 4.3.2 Using SAS; 4.4 Closing remarks

    5 Testing a covariate5.1 Introduction; 5.2 Testing a covariate; 5.2.1 Gray's method; 5.2.2 Pepe and Mori's method; 5.3 Software and examples; 5.3.1 Using R; 5.3.2 Using SAS; 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; 6.2.1 Theoretical details; 6.2.2 Model-based estimation of the CIF; 6.2.3 Using R; 6.3 Cox model and competing risks; 6.4 Checking the model assumptions; 6.4.1 Proportionality of the cause-specific hazards; 6.4.2 Proportionality of the hazards of the CIF

    6.4.3 Linearity assumption6.5 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; 7.3.1 General formulae; 7.3.2 Comparing cause-specific hazards; 7.3.3 Comparing hazards of the subdistributions; 7.3.4 Probability of event when the exponential distribution is not a valid assumption; 7.4 Examples; 7.4.1 Introduction; 7.4.2 Comparing the cause-specific hazard; 7.4.3 Comparing the hazard of the subdistribution; 7.5 Closing remarks

    8 Other issues in competing risks
  2. An Akaike information criterion for multiple event mixture cure models
    Author: Dirick, Lore; Claeskens, Gerda; Baesens, Bart
    Published: 2014
    Publisher:  KU Leuven, Fac. of Economics and Business, Leuven

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: KBI ; 1418
    Subjects: Akaike information criterion; Competing risks; EM-algorithm; Mixture cure model; Model selection
    Scope: Online-Ressource (28 S.), graph. Darst.
  3. A hierarchical mixture cure model with unobserved heterogeneity for credit risk
    Author: Dirick, Lore; Claeskens, Gerda; Vasnev, Andrey; Baesens, Bart
    Published: [2020]
    Publisher:  KU Leuven, Faculteit Economie en Bedrijfswetenschappen, Leuven, België

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 598
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: KBI ; KBI_20, 07
    Subjects: Credit risk modeling; Competing risks; EM-algorithm; Mixture cure model; Survival analysis; Unobserved heterogeneity
    Scope: 1 Online-Ressource (circa 30 Seiten), Illustrationen
  4. Private health investments under competing risks
    evidence from malaria control in Senegal
    Author: Rossi, Pauline; Villar, Paola
    Published: [2017]
    Publisher:  Paris-Jourdan Sciences Economiques, Paris

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 331 (2017,50)
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working paper / Paris School of Economics ; no 2017, 50
    Subjects: Health expenses; Malaria; Africa; Human capital; Competing risks
    Scope: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  5. Penalized weigted competing risks models based on quantile regression
    Author: Li, Erqian; Härdle, Wolfgang; Dai, Xiaowen; Tian, Maozai
    Published: [2021]
    Publisher:  International Research Training Group 1792, Berlin

    The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods, due to large numbers of irrelevant... more

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 744
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    The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods, due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators including consistency and asymptotic normality of non-penalized estimator and consistency of variable selection are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. A real data about bone marrow transplant (BMT) is also analyzed to illustrate the application of proposed procedure.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/235867
    Series: IRTG 1792 discussion paper ; 2021, 013
    Subjects: Competing risks; Cumulative incidence function; Kaplan-Meier estimator; Redistribution method
    Scope: 1 Online-Ressource (circa 28 Seiten), Illustrationen