Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. The book describes simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences.
Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model
Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model
This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.
This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences.
In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health.
This book proposes a systematic approach to building such models based on standard principles of statistical modeling.
This book is the first focused on this topic, and uses real data and software to illustrate the methods involved.
The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. T
Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data.
The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians.