Bayesian Survival, Longitudinal, and Joint Models with INLA

This book provides a comprehensive and practical guide to fitting complex Bayesian survival, longitudinal and joint models using the Integrated Nested Laplace Approximations (INLA) methodology, a powerful and computationally efficient alternative to traditional MCMC methods.Aimed at graduate students, researchers, and applied statisticians in biostatistics, epidemiology, and public health, this book addresses the critical challenge of analyzing high-dimensional and correlated data.It demonstrates how to move beyond the computational limitations of conventional methods, enabling the analysis of sophisticated models that were previously out of reach. Through a series of clear, fully reproducible examples, readers will learn to:- Implement a wide range of survival models, including proportional hazards, competing risks, multi-state, cure, and frailty models. - Fit various longitudinal models for continuous, count, binary, semicontinuous, and ordinal data. - Construct and interpret joint m