Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model, 1st Edition (Paperback) book cover

Flexible Parametric Survival Analysis Using Stata

Beyond the Cox Model, 1st Edition

By Patrick Royston, Paul C. Lambert

Stata Press

339 pages

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Paperback: 9781597180795
pub: 2011-08-04
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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.

Table of Contents



A brief review of the Cox proportional hazards model

Beyond the Cox model

Why parametric models?

Why not standard parametric models?

A brief introduction to stpm

Basic relationships in survival analysis

Comparing models

The delta method

Ado-file resources

How our book is organized

Using stset and stsplit

What is the stset command?

Some key concepts

Syntax of the stset command

Variables created by the stset command

Examples of using stset

The stsplit command


Graphical introduction to the principal datasets


Rotterdam breast cancer data

England and Wales breast cancer data

Orchiectomy data


Poisson models


Modeling rates with the Poisson distribution

Splitting the time scale

Collapsing the data to speed up computation

Splitting at unique failure times

Comparing a different number of intervals

Fine splitting of the time scale

Splines: Motivation and definition

FPs: Motivation and definition


Royston–Parmar models

Motivation and introduction

Proportional hazards models

Selecting a spline function

PO models

Probit models

Royston–Parmar (RP) models

Concluding remarks

Prognostic models


Developing and reporting a prognostic model

What does the baseline hazard function mean?

Model selection

Quantitative outputs from the model

Goodness of fit

Out-of-sample prediction: Concept and applications

Visualization of survival times


Time-dependent effects



What do we mean by a TD effect?

Proportional on which scale?

Poisson models with TD effects

RP models with TD effects

TD effects for continuous variables

Attained age as the time scale

Multiple time scales

Prognostic models with TD effects


Relative survival


What is relative survival?

Excess mortality and relative survival

Motivating example

Life-table estimation of relative survival

Poisson models for relative survival

RP models for relative survival

Some comments on model selection

Age as a continuous variable

Concluding remarks

Further topics


Number needed to treat

Average and adjusted survival curves

Modeling distributions with RP models

Multiple events

Bayesian RP models

Competing risks

Period analysis

Crude probability of death from relative survival models

Final remarks


Author index

Subject index

About the Authors

Patrick Royston is a senior medical statistician at the Medical Research Council, London, UK. He has published research papers on a variety of topics in leading statistics journals. His key interests include multivariable modeling and validation, survival analysis, design and analysis of clinical trials, and statistical computing and algorithms. He is an associate editor of the Stata Journal.

Paul Lambert is a reader in medical statistics at Leicester University, UK. His main interest is in the development and application of statistical methods in population-based cancer research and related fields. He has published widely in leading statistical and medical journals.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Biostatistics