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192.168.6.56/handle/123456789/55162Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Takeshi Emura • Yi-Hau Chen | - |
| dc.date.accessioned | 2019-03-19T07:07:17Z | - |
| dc.date.available | 2019-03-19T07:07:17Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.isbn | 978-981-10-7164-5 | - |
| dc.identifier.uri | http://10.6.20.12:80/handle/123456789/55162 | - |
| dc.description | This book introduces copula-based statistical methods to analyze survival data involving dependent censoring. This book explains why the problem of dependent censoring arises in medical research, and illustrates how copula-based statistical methods remedy the problem. This book introduces a variety of copula-based methods to deal with dependent censoring, including the copula-graphic estimator, parametric/semi-parametric maximum likelihood estimators, univariate selection method, and prediction method. This book also introduces the basic theory of copulas for modeling bivariate survival data. There are many general books on survival analysis such as Kalbfleisch and Prentice (2002), Lawless (2003), Klein and Moeschberger (2003), and Collett (2003, 2015). These books focus on the standard statistical methods that have been developed under the assumption of independent censoring. Nonetheless, all these books mention the importance of scrutinizing the independent censoring assumption when applying the standard methods to real data. Kalbfleisch and Prentice (2002), Lawless (2003), and Klein and Moeschberger (2003) provide competing risks approaches to deal with dependent censoring without using copulas. In his latest edition of “Modelling Survival Data in Medical Research,” Collett (2015) added a new chapter, “Dependent Censoring,” where some techniques of dealing with dependent censoring are introduced. Our book introduces a variety of copula-based statistical methods that are not discussed in the above-listed books. Our emphasis is placed on survival data arising from medical studies. I hope that this book appeals to those working as (bio) statisticians in medical and pharmaceutical institutes. Of course, statistical methods presented in this book can be applied to many fields, especially in engineering and econometrics where survival analysis plays an important role. | - |
| dc.language | en | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Survival Data | en_US |
| dc.title | Analysis of Survival Data with Dependent Censoring | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | Population Studies | |
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