Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.
There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.
Univariate Survival Analysis
Scenario
Survival Distributions
Continuous Time Parametric Inference
Continuous Time Non- and Semi-Parametric Methods
Discrete Time
Multivariate Survival Analysis
Multivariate Data and Distributions
Some Parametric Models
Frailty, Random Effects, and Copulas
Repeated Measures
Recurrent Events
Multi-State Processes
Competing Risks
Continuous Failure Times and Their Causes
Continuous Time Parametric Inference
Latent Lifetimes
Continuous Time Non- and Semi-Parametric Methods
Discrete Lifetimes
Latent Lifetimes Identifiability Crises
Counting Processes in Survival Analysis
Some Basic Concepts
Survival Analysis
Non- and Semi-Parametric Methods
Appendices
Terms, Notations, and Abbreviations
Basic Likelihood Methods
Some Theory for Partial Likelihood
Numerical Optimisation of Functions
References
Epilogue
Index
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