Stella & Rose's Books

Contents

• List of Tables
• List of Figures
• Preface
• 1 Introduction
• 1.1 Goals
• 1.2 A brief review of the Cox proportional hazards model
• 1.3 Beyond the Cox Model
• 1.4 Why parametric models?
• 1.5 Why not standard parametric models?
• 1.6 A brief introduction to stpm2
• 1.7 Basic relationships in survival analysis
• 1.8 Comparing models
• 1.9 The delta method
• 1.10 Ado-file resources
• 1.11 How our book is organised
• 2 Using stset and stsplit
• 2.1 What is the stset command?
• 2.2 Some key concepts
• 2.3 Syntax of the stset command
• 2.4 Variables created by the stset command
• 2.5 Examples of using stset
• 2.6 The stsplit command
• 1.7 Conclusion
• 3 Graphical introduction to the principal datasets
• 3.1 Introduction
• 3.2 Rotterdam breast cancer data
• 3.3 England and Wales breast cancel data
• 3.4 Orchiectomy data
• 3.5 Conclusion
• 4. Poisson models
• 4.1 Introduction
• 4.2 Modelling rates with the Poisson distribution
• 4.3 Splitting the time scale
• 4.4 Collapsing the data to speed up computation
• 4.5 Spitting at unique failure times
• 4.6 Comparing a different number of intervals
• 4.7 Fine splitting of the time scale
• 4.8 Splines: Motivation and definition
• 4.9 FP's: Motivation and definition
• 4.10 Discussion
• 5 Royston-Parmar models
• 5.1 Motivation and introduction
• 5.2 Proportional hazards models
• 5.3 Selecting a spline function
• 5.4 PO Models
• 5.5 Pribit models
• 5.6 Royston=Parmar (RP) models
• 5.7 Concluding remarks
• 6 Prognostic models
• 6.1 Introduction
• 6.2 Developing and reporting a prognostic model
• 6.3 What does the baseline
• 6.4 Model selection
• 6.5 Quantitative outputs from the model
• 6.6 Goodness of fit
• 6.7 Discrimination and explained variation
• 6.8 Out-of-Sample prediction: Concept and applications
• 6.9 Visualization of survival times
• 6.10 Discussion
• 7 Time-dependent effects
• 7.1 Introduction
• 7.2 Definitions
• 7.3 What do we mean by a TD effect?
• 7.4 Proportional on which scale?
• 7.5 Poisson models with TD effects
• 7.6 RP models with TD effects
• 7.7 TD effects for continuous variables
• 7.8 Attained age as the time scale
• 7.9 Multiple time scales
• 7.10 Prognostic models with TD effects
• 7.11 Discussion
• 8 Relative survival
• 8.1 Introduction
• 8.2 What is relative survival
• 8.3 Excess mortality and relative survival
• 8.4 Motivating example
• 8.5 Life-table estimation of relative survival
• 8.6 Poisson models for relative survival
• 8.7 RP models with relative survival
• 8.8 Some comments on model selection
• 8.9 Age as a continuous variable
• 8.10 Concluding remarks
• 9 Further topics
• 9.1 Introduction
• 9.2 Number needed to treat
• 9.3 Average and adjusted survival curves
• 9.4 Modelling distributions with RP models
• 9.5 Multiple events
• 9.6 Bayesian RP Models
• 9.7 Competing risks
• 9.8 Period analysis
• 9.9 Crude probability of death from relative survival models
• 9.10 Final remarks
• References
• Author Index
• Subject Index