1st Edition

Flexible Parametric Survival Analysis Using Stata Beyond the Cox Model

By Patrick Royston, Paul C. Lambert Copyright 2011
    339 Pages
    by Stata Press

    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.

    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


    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.