Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model

by Patrick Royston, Paul C. Lambert

Researchers wishing to fit regression models to survival data have long faced the difficult task of choosing between the Cox model and a parametric survival model, such as Weibull. Cox models are fit using Stata’s stcox command, and parametric models are fit using streg, which offers five parametric forms in addition to Weibull. While the Cox model makes minimal assumptions about the form of the baseline hazard function, prediction of hazards and other related functions for a given set of covariates is hindered by this lack of assumptions; the resulting estimated curves are not smooth and do not possess information about what occurs between the observed failure times. Parametric models offer nice, smooth predictions by assuming a functional form of the hazard, but often the assumed form is too structured for use with real data, especially if there exist significant changes in the shape of the hazard over time.

Flexible Parametric Survival Analysis Using Beyond the Cox Model is concerned with obtaining a compromise between Cox and parametric models that retains the desired features of both types of models. The book is aimed at researchers who are familiar with the basic concepts of survival analysis and with the stcox and streg commands in Stata. As such, it is an excellent complement to An Introduction to Survival Analysis Using Stata by Cleves, Gould, and Marchenko.

582 pages, Kindle Edition

First published August 4, 2011