Flexible Parametric Survival Analysis Using Stata Beyond the Cox Model

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.

Introduction Goals 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 Conclusion Graphical introduction to the principal datasets Introduction Rotterdam breast cancer data England and Wales breast cancer data Orchiectomy data Conclusion Poisson models Introduction 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 Discussion 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 Introduction 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 Discussion Time-dependent effects Introduction Definitions 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 Discussion Relative survival Introduction 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 Introduction 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 References 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.