Frailty models in survival analysis / Andreas Wienke.
The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frai...
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| Format: | Electronic eBook |
| Language: | English |
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Boca Raton :
CRC Press,
©2011.
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| Series: | Chapman & Hall/CRC biostatistics series.
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MARC
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| 100 | 1 | |a Wienke, Andreas. | |
| 245 | 1 | 0 | |a Frailty models in survival analysis / |c Andreas Wienke. |
| 260 | |a Boca Raton : |b CRC Press, |c ©2011. | ||
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| 520 | |a The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.--From the publisher's website. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front cover; Contents; List of Tables; List of Figures; Preface; Chapter 1: Introduction; Chapter 2: Survival Analysis; Chapter 3: Univariate Frailty Models; Chapter 4: Shared Frailty Models; Chapter 5: Correlated Frailty Models; Chapter 6: Copula Models; Appendix A; References; Back cover. | |
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Failure time data analysis |x Mathematics. | |
| 650 | 0 | |a Survival analysis (Biometry) |x Mathematics. | |
| 650 | 0 | |a Mortality |x Mathematical models. | |
| 650 | 0 | |a Demography |x Mathematics. | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Survival analysis (Biometry) | |
| 650 | 0 | |a Mortality. | |
| 650 | 0 | |a Demography. | |
| 650 | 7 | |a Survival analysis (Biometry) |2 fast | |
| 650 | 7 | |a Mortality |2 fast | |
| 650 | 7 | |a Mathematical models |2 fast | |
| 650 | 7 | |a Demography |2 fast | |
| 650 | 7 | |a Demography |x Mathematics |2 fast | |
| 650 | 7 | |a Mortality |x Mathematical models |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Wienke, Andreas. |t Frailty models in survival analysis. |d Boca Raton, FL : CRC Press, ©2011 |z 9781420073881 |w (DLC) 2010021869 |w (OCoLC)640916554 |
| 830 | 0 | |a Chapman & Hall/CRC biostatistics series. | |
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