Dynamic Regression Models for Survival Data

This book shows the usefulness of counting processes and marked poit processes when dealing with non-parametric and semi-parametric statistical problems.

A key issue in this book is extensions of the Cox model and alternative models with most of them having the specific aim to be able to deal with time-varying effects of covariates in regression analysis Includes supplementary material: sn.pub/extras

Contenu
Probabilistic background.- Estimation for filtered counting process data.- Nonparametric procedures for survival data.- Additive Hazards Models.- Multiplicative hazards models.- Multiplicative-Additive hazards models.- Accelerated failure time and transformation models.- Clustered failure time data.- Competing Risks Model.- Marked point process models.