Provides an overview of the methodology and applications of likelihood methods in survival analysis. It covers all the important topics, including competing risks and joint models. It includes lots of examples to illustrate the methods, and R code for their implementation. A supplementary R package includes all code and data.
Provides an overview of the methodology and applications of likelihood methods in survival analysis. It covers all the important topics, including competing risks and joint models. It includes lots of examples to illustrate the methods, and R code for their implementation. A supplementary R package includes all code and data.
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
Inhaltsangabe
1. Introduction. 2. Semi-parametric Cox Model with Interval Censoring. 3. Extension to Include Truncation. 4. Extension to Include A Cured Fraction. 5. Stratified Cox models under interval censoring. 6. Cox Models with Time-Varying Covariates under Right Censoring. 7. Copula Cox Models for Dependent Right Censoring. 8. Additive hazards model. 9. Parametric Survival Models for Competing Risks Data.
1. Introduction. 2. Semi-parametric Cox Model with Interval Censoring. 3. Extension to Include Truncation. 4. Extension to Include A Cured Fraction. 5. Stratified Cox models under interval censoring. 6. Cox Models with Time-Varying Covariates under Right Censoring. 7. Copula Cox Models for Dependent Right Censoring. 8. Additive hazards model. 9. Parametric Survival Models for Competing Risks Data.
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