Likelihood Methods in Survival Analysis
Language: English
Published by CRC Press, 2024
- Softcover
- New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Jun Ma, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, AustraliaAnnabel Webb, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, AustraliaMalcolm Hudson, S.
Seller Inventory # 1571869609
- Title
- Likelihood Methods in Survival Analysis
- Author
- Jun Ma (Department of Statistics, Faculty of Science and Engineering, Macquarie University)|Annabel Webb (Macquarie University, Australia)|Harold Malcolm Hudson
- Publisher
- CRC Press
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0815362846
- ISBN 13
- 9780815362845
Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.
Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.
Features
- Provides a broad and accessible overview of likelihood methods in survival analysis
- Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks
- Includes many examples using real data to illustrate the methods
- Includes integrated R code for implementation of the methods
- Supplemented by a GitHub repository with datasets and R code
The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.
"Synopsis" may belong to another edition of this title.
About the Author
Jun Ma, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, Australia
Annabel Webb, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, Australia
Malcolm Hudson, School of Mathematical and Physical Sciences, Macquarie University & NHMRC Clinical Trial Centre, University of Sydney, Sydney, Australia
"About the title" may belong to another edition of this title.
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