Likelihood Methods in Survival Analysis: With R Examples

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.